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The book presents a comprehensive and interdisciplinary exploration of the impact of
AI on various sectors of society to foster a greater understanding of the opportunities
and challenges presented by this transformative technology. It explores the impact AI
has had on varied sectors of society, including healthcare, education, the workplace,
and the economy. It provides a holistic view of this fast- growing technology by criti-
cal study of the possible benets and drawbacks linked with the application of AI in
many industries. The book also examines the ethical, social, and economic implica-
tions of AI and the potential risks and challenges associated with its use.
• Focuses on the future inuence of AI, providing insights into how it could
disrupt several industries and change the way we live, work, and connect
with one another.
• Explores how AI can be used to tackle global issues such as climate change,
food security, and public health concerns.
• Offers case studies and specic examples of how articial intelligence is
being employed in many industries, covering both successes and failures.
• Investigates cutting- edge technology breakthroughs in AI and how they can
be used to improve efciency, productivity, and performance across mul-
tiple industries.
• Understands the limitations and potential biases of articial intelligence, as
well as the signicance of human monitoring and accountability.
The book is intended for researchers, practitioners, policymakers, and students who
are interested in understanding the nature and role of AI with regard to different sec-
tors of society.
Impact of Articial
Intelligence on Society
Impact of Articial
Intelligence on Society
Edited by
Sumit Tripathi
Joanna Rosak-Szyrocka
Designed cover image: PopTika/Shutterstock
First edition published 2025
by CRC Press
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and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2025 selection and editorial matter, Sumit Tripathi and Joanna Rosak-Szyrocka; individual chapters,
the contributors
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ISBN: 978-1-032-64324-3 (hbk)
ISBN: 978-1-032-64449-3 (pbk)
ISBN: 978-1-032-64450-9 (ebk)
DOI: 10.1201/9781032644509
Typeset in Times
by SPi Technologies India Pvt Ltd (Straive)
v
Contents
Preface ......................................................................................................................vii
About the Authors ...................................................................................................viii
List of Contributors ...................................................................................................ix
Introduction ...............................................................................................................xi
Chapter 1 Disruptive Innovation in Medical Image Segmentation: A
Comparative Study of Traditional and AI-Based Approaches .............1
Sumit Tripathi and Joanna Rosak-Szyrocka
Chapter 2 Applications of Transformer in Medical Imaging: A Review ............19
Satish Kumar
Chapter 3 Future of Learning: Adaptive Learning Systems Based on
Language Generative Models in Higher Education ...........................31
Cristina Dumitru
Chapter 4 Articial Intelligence (AI) Integration in Higher Education:
Navigating Opportunities and Ethical Frontiers in Education
with Advanced Technologies ..............................................................43
Seema Yadav
Chapter 5 The Future of Higher Education: Using AI in Universities to
Improve Learning Outcomes and Operational Efciency ..................60
Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma,
and Bhupendra Kumar
Chapter 6 AI in Academic Research: Advances, Opportunities, and
Challenges .......................................................................................... 81
Almula Umay Karamanlıoglu
Chapter 7 Transforming Education through AI-Enhanced Content Creation
and Personalized Learning Experiences ............................................. 98
M. Shanmuga Sundari, Harshini Reddy Penthala,
and Anand Nayyar
vi Contents
Chapter 8 Exploring the Interplay of Educational Social Media Usage,
Procrastination, and Subjective Well-being in the Context of
Industry 5.0 Education .....................................................................119
Pallavi Patwari and Aparna Vajpayee
Chapter 9 Advancements and Challenges in Fraudulent Message
Detection ..........................................................................................134
Nawnit Kumar, Shalabh K. Mishra, and Ramesh Nuthakki
Chapter 10 Articial Intelligence of Things (AIoT)-Based Telehealth
System Using Healthy Pi .................................................................. 143
Arif Hussain, Abid Iqbal, Muhammad Abeer Irfan,
Irfan Ahmed, and Amaad Khalil
Chapter 11 Articial Intelligence for Social Good, Disaster Relief, Poverty
Alleviation, and Environmental Sustainability: A New Era of
Innovative Solution and Global Impact ............................................ 156
V. Selvalakshmi, Durdana Ovais, Shubhra Bhatia,
and S. Gayathri
Chapter 12 Digital Innovations for Increasing Financial Inclusion: CBDC,
Cryptocurrency, Embedded Finance, Articial Intelligence,
WaaS, Fintech, Bigtech, and DeFi ...................................................174
Peterson K. Ozili
Index ...................................................................................................................... 185
vii
Preface
The assimilation of Articial Intelligence (AI) into several domains of contemporary
society signies a turning point in the history of technological progress. The goal
of this book, Impact of Articial Intelligence on Society, is to help readers under-
stand how AI is affecting various facets of human life. It is critical to analyse and
fully appreciate the complex implications AI has on our shared future, given that
we nd ourselves at the nexus of previously unimaginable technology capabilities
and societal ramications. This book began with the realization that AI has signi-
cantly more transformational capacity outside of its technological realm. Articial
intelligence has implications for social, ethical, political, and economic spheres as it
permeates industries, economies, and everyday life. This collection of observations,
analyses, and empirical research aims to clarify the subtleties of AI’s inuence by
avoiding exaggeration and conjecture and instead basing conversations on factual
ndings. With a critical and progressive lens, this book seeks to further a comprehen-
sive knowledge of the potential and problems associated with integrating AI into our
global social fabric.
We have brought together responses from specialists in the domains of technol-
ogy, economics, sociology, and ethics to promote a holistic analysis of the implica-
tions of articial intelligence. Through the synthesis of multiple viewpoints, we
strive to create a comprehensive story that appeals to readers of different backgrounds
while maintaining the in- depth analysis. It is our aim that this book will be a useful
tool for people, governments, and academics as AI develops and spreads into new
areas, encouraging a critical and informed discussion about the enormous social
ramications of AI. To sum up, Impact of AI on Society hopes to serve as a scholarly
compass that leads readers through the complex terrain that AI has carved out,
encouraging a thorough comprehension of its implications on the dynamics of our
globalized society.
viii
About the Authors
Dr. Sumit Tripathi is an accomplished associate profes-
sor at the Goa Institute of Management in Goa, India,
bringing over 15 years of academic and research expe-
rience to his role. He earned his post- doctorate from
Linkoping University, Sweden, and completed his PhD
at the Indian Institute of Technology (BHU), Varanasi.
Dr. Tripathi’s research interests encompass Articial
Intelligence, Machine Learning, Deep Learning,
Computer Vision, and Biomedical Image Processing.
His expertise is evident through numerous high- impact
research papers published in esteemed conferences and journals, as well as patents in
Biomedical Image Processing using Deep Learning- based Techniques. As a lifetime
member of the Indian Society of Technical Education (ISTE), Dr. Tripathi is dedi-
cated to advancing the elds of engineering education and research.
Dr. Joanna Rosak- Szyrocka is assistant professor,
Erasmus+ coordinator at the Faculty of Management,
Czestochowa University of Technology, Poland. She
specialized in the elds of digitalization, industry 5.0,
quality 4.0, education, IoT, AI, and quality management.
She completed a research internship at the University of
Zilina, Slovakia, and at Silesia University of Technology,
Poland. She has been a participant in multiple Erasmus+
teacher mobility programs: Italy, UK, Slovenia, Hungary,
Czech Republic, Slovakia, and France. She cooper-
ates with many universities both in the country (University of Szczecin, Rzeszów
University of Technology, Silesian University of Technology) and abroad (includ-
ing the University of Tabuk, Saudi Arabia; Széchenyi István University, Hungary;
University Faisalabad, Pakistan; University of Humanities, China, University of
Technology Sydney, Australia; Bucharest University of Economic Studies, Romania;
and Federal University Dutse, Nigeria). She has also served as a member of the edito-
rial boards and reviewer boards for several prestigious journals.
ix
Irfan Ahmed
University of Engineering &
Technology Peshawar
Peshawar, Pakistan
Shubhra Bhatia
Bhopal School of Social Sciences
Bhopal, India
Cristina Dumitru
The National University of Science
and Technology POLITEHNICA
Bucharest, Pitești University Centre
Romania
S. Gayathri
Rajalakshmi School of Business
Chennai, India
Arif Hussain
University of Engineering &
Technology Peshawar
Peshawar, Pakistan
Abid Iqbal
University of Engineering &
Technology Peshawar
Peshawar, Pakistan
Muhammad Abeer Irfan
University of Engineering &
Technology Peshawar
Peshawar, Pakistan
Almula Umay Karamanlıoglu
Baskent University
Turkey
Amaad Khalil
University of Engineering &
Technology Peshawar
Peshawar, Pakistan
Bhupendra Kumar
School of Computer Science and
Applications, IIMT University
Meerut, UP, India
Nawnit Kumar
Sri Guru Tegh Bahadur Institute of
Management & IT
New Delhi, India
Satish Kumar
Department of Information Technology
BGSB University Rajouri
J&K, India
Shalabh K. Mishra
Bharati Vidyapeeth’s College of
Engineering
New Delhi, India
Anand Nayyar
School of Computer Science
DuyTan University
Da Nang, Viet Nam
Ramesh Nuthakki
Atria Institute of Technology
Bengaluru, India
Durdana Ovais
The BSSS Institute of Advanced
Studies
Bhopal, India
Peterson K. Ozili
Central Bank of Nigeria
Nigeria
Pallavi Patwari
School of Liberal Arts and Management
Studies
P P Savani University
Gujarat, India
List of Contributors
x List of Contributors
Harshini Reddy Penthala
BVRIT HYDERABAD College of
Engineering for Women
India
Joanna Rosak- Szyrocka
Czestochowa University of Technology
Faculty of Management Department
of Production Engineering and Safety
Poland
V. Selvalakshmi
Department of Management Studies
Velammal College of Engineering and
Technology
Madurai, India
Kewal Krishan Sharma
School of Computer Science and
Applications
IIMT University
Meerut, UP, India
Vikas Sharma
School of Computer Science and
Applications
IIMT University
Meerut, UP, India
M. Shanmuga Sundari
BVRIT HYDERABAD College of
Engineering for Women
India
Sumit Tripathi
Big Data Analytics
Goa Institute of Management
Goa, India
Aparna Vajpayee
School of Liberal Arts and Management
Studies
P P Savani University
Gujarat, India
Tarun Kumar Vashishth
School of Computer Science and
Applications
IIMT University
Meerut, UP, India
Seema Yadav
Department of Education Bhopal
School of Social Sciences
Bhopal, India
xi
Introduction
The Impact of Articial Intelligence on Society presents a comprehensive exploration
of AI’s transformative role across diverse sectors. Chapter 1 investigates AI’s disrup-
tive potential in medical image segmentation, comparing traditional methods with
AI- based approaches to enhance diagnostic accuracy and patient care. In Chapter 2,
the applications of transformer models in medical imaging are reviewed, showcasing
their ability to revolutionize tasks such as image classication and object detection.
Chapter 3 explores the future of learning in higher education, highlighting adap-
tive learning systems based on language generative models to personalize learning
experiences and optimize curriculum design. Moving to AI integration in higher
education, Chapter 4 navigates through opportunities and ethical considerations,
emphasizing responsible AI use for improved educational outcomes. Chapter 5 dis-
cusses how AI can revolutionize higher education by improving learning outcomes
and operational efciency in universities. Chapter 6 delves into AI’s role in academic
research, discussing advances and challenges in data analysis, predictive modeling,
and interdisciplinary collaboration. Continuing the discussion on AI in education,
Chapter 7 explores AI- enhanced content creation and personalized learning experi-
ences to foster greater student engagement and comprehension. Chapter 8 investi-
gates the interplay of educational social media usage, procrastination, and well- being
in digital learning environments. Chapter 9 examines advancements and challenges
in fraudulent message detection, emphasizing AI’s role in mitigating online threats.
Focusing on healthcare, Chapter 10 explores IoT- based telehealth systems for remote
patient monitoring and diagnosis. In Chapter 11, AI’s potential for social good is
highlighted, addressing disaster relief, poverty alleviation, and environmental sus-
tainability through innovative AI- driven solutions. Lastly, Chapter 12 explores digital
innovations for increasing nancial inclusion, showcasing AI’s role in empowering
underserved populations and fostering economic growth. Through these chapters,
readers gain insights into AI’s multifaceted applications, its potential to drive positive
societal impact, and the challenges and opportunities inherent in its adoption across
industries.
1DOI: 10.1201/9781032644509-1
1.1 INTRODUCTION TO SEGMENTATION
In the expansive domain of present- day healthcare, the fusion of technology and
medicine has yielded unparalleled opportunities for transforming the processes of
diagnosis, treatment, and patient care. The subject of medical image segmentation
has witnessed signicant improvements in recent years, which have had a dramatic
impact on the eld of synergy. This chapter explores the complex realm of pixel-
by- pixel analysis, focusing on the signicant role that medical image segmentation
plays in the advancement of precision medicine (Balafar et al. 2010). The task of
medical image segmentation involves the division or partitioning of medical images
into distinct sections that hold signicance for diagnosis and interpretation (Abler,
Rockne, and Büchler 2019). This technique enables the segregation and analysis
of distinct structures or abnormalities present in the images, regardless of their
origin from magnetic resonance imaging (MRI), computed tomography (CT), ultra-
sound, or other imaging modalities. This procedure forms the basis for numerous
clinical applications, ranging from the detection of tumors in radiological scans
to the segmentation of anatomical structures for the purpose of surgical planning.
The signicance of medical image segmentation is mostly attributed to its ability to
enhance individualized healthcare through the utilization of data- driven approaches.
This technology enables healthcare practitioners to make well- informed judgments,
develop customized treatment strategies, and accurately forecast patient outcomes
Disruptive Innovation
in Medical Image
Segmentation
A Comparative Study of
Traditional and AI-Based
Approaches
Sumit Tripathi
Goa Institute of Management Poriem, Sanquelim, India
Joanna Rosak- Szyrocka
Czestochowa University of Technology, Poland
1
2 Impact of Articial Intelligence on Society
with exceptional accuracy (Bauer et al. 2013). The signicance of this technique
on patient care cannot be understated, as it is utilized for many purposes such as
outlining malignant masses, distinguishing neuronal structures in brain scans, and
segmenting cardiac regions for accurate diagnosis. The objective of segmentation
is to demarcate the borders of objects by assigning a distinct label to each pixel or
voxel. Pixels or voxels that possess identical labels exhibit comparable attributes or
belong to the identical classication (Tripathi and Sharma 2021).
Image segmentation can be categorized into three primary methods: manual seg-
mentation, semi- automatic segmentation, and fully automatic segmentation. Manual
segmentation involves radiologists and doctors with expertise who delineate specic
regions of interest (ROI). However, this approach is time- consuming and impractical
for large datasets, and it can introduce signicant variability. Semi- automatic meth-
ods combine manually designed elements with human operator intervention during
processing stages. The emergence of fully automatic techniques, driven by the grow-
ing importance of computer- based systems, allows for complete control of feature
extraction and segmentation. Deep learning, in particular, has played a crucial role in
developing entirely automated methods capable of producing reliable and precise
results. In the eld of biomedical image segmentation, deep learning- based methods
are attracting signicant interest, particularly for diagnosing pathological tissues
(Tripathi et al. 2021). Addressing the challenge of limited medical data availability
has become a priority in the design of these networks. These approaches can autono-
mously extract features and learn. Deep learning- based architectures for image pro-
cessing primarily employ supervised learning approaches.
This chapter will undertake a thorough examination of the fundamental prin-
ciples, methodology, and practical applications pertaining to the process of medi-
cal image segmentation. The present chapter explores the progression of algorithms
and approaches that have undergone development over time, enabling the inter-
pretation and extraction of useful information from images with unparalleled
precision.
1.2 CLASSICAL METHODS OF MEDICAL IMAGE SEGMENTATION
The segmentation of medical images is an essential task within the eld of medi-
cal image analysis, as it holds signicant importance in various areas like disease
diagnosis, treatment planning, and medical research. Traditional approaches to medi-
cal segmentation of images often depend on existing methodologies in the elds of
image processing and computer vision. The primary purpose of segmentation is to
partition the objects inside an image. In the context of medical image segmenta-
tion, the objective is twofold: to examine the anatomical structure and to identify the
Region of Interest (ROI), which involves locating tumors, lesions, and other irregu-
larities. Tissue volume measurement is employed to assess the growth of tumors,
as well as the reduction in tumor size resulting from treatment. Additionally, it aids
in treatment planning before the administration of radiation therapy, namely in the
computation of radiation dosage. The categorization of methods for segmenting the
image can be broadly outlined as follows:
Disruptive Innovation in Medical Image Segmentation 3
1.2.1 Methods that Rely on GRay-level FeatuRes
These are the methods that take in to account the gray- level features for segmentation
of medical images. Histogram- based segmentation is one such approach.
1.2.1.1 Histogram-Based Method for Segmentation
Within the eld of image processing, a histogram serves as a visual representation
that illustrates the distribution of pixel intensity values within an image. The provided
data pertains to the relative brightness or darkness of pixels within different regions
of the image. Through the examination of the histogram, one can acquire valuable
knowledge pertaining to the contrast, brightness, and distribution of objects within
the image. Histogram- based segmentation involves the analysis of pixel intensity
values in order to identify and delineate distinct regions or segments (Caraiman and
Manta 2014). One commonly employed technique is thresholding, which involves
the selection of a threshold value to categorize pixels as belonging to a particular
segment based on whether their intensity values exceed or fall below this threshold.
In addition, adaptive thresholding can be employed, wherein the determination of the
threshold value is contingent upon the localized attributes of the image. The thresh-
olding function can be expressed by Equation 1.1:
uw
w
xy
xy
xy
,
,
,
1
0
(1.1)
The output pixel at coordinate (x, y) is denoted as ux, y. It is determined by the pixel
value of the input image, wx, y and the threshold value, θ. This thresholding operation
holds good for binary segmentation. Figure 1.1 shows the results after thresholding
operation is performed over the brain and skin cancer images.
1.2.2 Methods that Rely on edGe detection FoR seGMentation
Edge detection algorithms are a prevalent approach to image segmentation. These
methods concentrate on detecting image borders or edges, which can then be uti-
lized to segregate objects or regions of interest (Xu et al. 2021). The edge detection
approach identies differences in properties such as gray levels or colors, which
frequently represent transitions between various regions. This method effectively
divides an image based on object boundaries by segmenting it by pinpointing these
transition zones. Edge detection- based methods are implemented in the following
steps:
a) Edge Detection: To locate edges in an image, use a derivative operator.
b) Edge Strength Measurement: Determine the strength of detected edges by
measuring the amplitude of gradient.
c) Edge Pruning: Remove weak edges by keeping only those whose magni-
tude exceeds a predened threshold (T).
4 Impact of Articial Intelligence on Society
d) Crack Edge Analysis: Determine the positions of crack edges and decide
whether to keep or reject them based on the condence of their neighboring
edges.
e) Closed Boundary Identication: Repeat steps 3 and 4 with different
threshold settings to identify closed borders and segment the image.
Prewitt, Sobel, Roberts (1st derivative type), Laplacian (2nd derivative type), Canny,
and Marr- Hildreth, Edge Tracing edge detectors are among the gradient (derivative)
function- based edge detection operators available. The goal of edge- based segmenta-
tion methods is to create object boundaries by connecting identied edges to form an
edge chain. A thresholding operation is used to remove false or weak edges during
this process. Edge- based segmentation results are presented in Figure 1.2 over brain
and skin cancer images, respectively.
FIGURE 1.1 Results of histogram-based thresholding: (a) and (c) are original brain tumor
and skin cancer images, (b) and (d) are segmented results of brain and skin cancer images.
Disruptive Innovation in Medical Image Segmentation 5
1.2.3 Methods that Rely on ReGion chaRacteRistics FoR seGMentation
A method for segmenting an image into regions or segments based on specic attri-
butes or features is called region- based segmentation, and it is used in computer
vision and image processing (Liu et al. 2021). Gathering together groups of pixels or
regions in an image that have similar characteristics—like color, texture, intensity, or
other visual aspects—is the aim of region- based segmentation. Numerous applica-
tions, such as object recognition, image analysis, and computer vision tasks, depend
on this process. An image is a composite made up of several different regions, each
acting as a separate component. When these separate parts or areas are put together,
they make up the entire image. Equation 1.2 can be used to represent the relationship
between regions:
XUXUXU UX I
ab
cz
.. (1.2)
FIGURE 1.2 Results of edge-based segmentation (a) and (c) are original brain tumor and
skin cancer images, (b) and (d) are segmented results of brain and skin cancer images.
6 Impact of Articial Intelligence on Society
where X denotes the various regions in the image I. Xa, Xb, Xc……Xz are the regions
which possess common characteristics. The region- based algorithms look for the
homogenous characteristics of regions for the segmentation task. The results of
region- based segmentation are shown in Figure 1.3. Region- based algorithms are
implemented in the following steps:
a) Extraction of Features: Extracting pertinent features from the image, such
as color, intensity, texture, or shape, is the rst step in the process. The vari-
ous regions of the image will be distinguished using these features.
b) Seed Selection: Choosing the initial seed points or regions is a common
rst step in region- based segmentation. These seeds can be selected auto-
matically or by hand according to predetermined standards. Seeds are the
beginning of a region’s growth or fusion.
FIGURE 1.3 Results of region-based segmentation: (a) and (c) are original brain tumor and
skin cancer images, (b) and (d) are segmented results of brain and skin cancer images.
Disruptive Innovation in Medical Image Segmentation 7
c) Region Growing or Splitting: There are two typical methods for segment-
ing data based on regions:
• Region Growing: This method begins with seed points and, if nearby
pixels share the selected feature(s), adds them to the region iteratively.
Until a halting condition is satised, this process keeps going.
• Region Splitting: This is also known as hierarchical segmentation,
is a technique that starts with the entire image as a single region and
divides it into smaller regions recursively according to how dissimilar
the feature(s) within the region are. A structure of regions resembling a
tree may arise from this hierarchical segmentation.
d) Merging and Post- processing: To obtain the required segmentation,
regions may need to be merged or rened after expanding or splitting. Small
or undesirable regions can be eliminated and the nal segmentation result
can be improved by using post- processing techniques.
e) Labeling: Provide the segmented regions labels so that they can be further
examined or used for object recognition.
1.2.4 split and MeRGe alGoRithM
An image can be segmented using the Split and Merge algorithm, which separates an
image into segments or regions according to predetermined criteria (An et al. 2015).
It works especially well for segmenting images that have areas with uniform texture,
color, or other attributes. The following steps constitute split and merge algorithm:
1. Initialization: The image is initially seen as a single region.
2. Splitting:
• Splitting Criteria: Assess the area to see if it can be divided into more
manageable sections. Usually, there are some criteria used to determine
this, like variations in the region’s color, intensity, or texture.
• Splitting Process: Divide the region into smaller sub- regions if the
requirements for splitting are satised. The region can be divided into
four equal quadrants to achieve this, or more complex methods such as
quadtree or octree decomposition can be used.
3. Merging:
• Merging Criteria: After splitting, determine whether the resulting sub-
regions should be merged. Examining the degree of similarity between
adjacent sub- regions is frequently one of the merging criteria. Sub-
regions are merged back together if they are sufciently similar to one
another.
• Procedure for Merging Regions: Adjacent regions that comply with the
merging criteria are combined. By taking this step, it is ensured that the
segmentation is not too nely tuned.
4. Iterative Process: Until no more splitting or merging is possible, steps 2
and 3 are repeated several times. A collection of regions that represent inter-
esting objects or features within the image is the end product.
8 Impact of Articial Intelligence on Society
5. Post- processing: To further rene the segmented regions, additional post-
processing steps may be carried out, depending on the application. This
could involve other improvements, such as boundary smoothing or noise
reduction.
The Split and Merge algorithm can be applied to a variety of image segmentation
tasks because it is versatile. This method, which is recursive in nature and divides
an image into homogeneous regions, is helpful for tasks like edge detection, object
recognition, and scene analysis. By selecting suitable split and merge criteria based
on the unique properties of the processed images, the algorithm can be tailored. The
results after applying the split and merge algorithm on brain MR images can be seen
in Figure 1.4.
1.3 ADVANCED DEEP LEARNING-BASED METHODS FOR IMAGE
SEGMENTATION
In the eld of medical image segmentation, convolutional neural networks, or CNNs,
have indeed become a disruptive technology (Krizhevsky, Sutskever, and Hinton
2017). Anatomical structures and lesions in medical images, including X- rays, CT
scans, MRIs, and histopathological slides, can be automatically and accurately seg-
mented by CNNs, a remarkable capability. Human error and variability in the inter-
pretation of medical images could be decreased by this technology. In remote or
underserved areas where access to medical specialists is limited, CNNs can be used
to provide specialized medical expertise. Rapid medical image processing by CNNs
enables more rapid treatment planning and diagnosis. Early identication of illnesses
and abnormalities can be detected by CNNs, which can lead to better patient out-
comes through early intervention (Badrinarayanan, Kendall, and Cipolla 2016). The
exploration of advanced deep learning for image segmentation continues to push
FIGURE 1.4 Results of split and merge algorithm-based segmentation: (a) original brain
tumor image and (b) segmented results of brain images.
Disruptive Innovation in Medical Image Segmentation 9
the envelope of what is possible and has the potential to completely transform how
humans view and work with visual data. The U- Net and GAN deep learning- based
architectures will be covered in more detail in this section.
1.3.1 u-net
In deep learning, U- Net (Ronneberger, Fischer, and Brox 2015) is a popular archi-
tecture for segmenting images. Olaf Ronneberger, Philipp Fischer, and Thomas Brox
rst described it in their paper “U- Net: Convolutional Networks for Biomedical
Image Segmentation,” published in 2015. U- Net has gained popularity in medi-
cal image analysis and is especially well- suited for tasks involving semantic and
instance segmentation. The combination of an encoder (contracting path) and a
decoder (expansive path) makes up the U- shaped structure of the U- Net architecture
as shown in Figure 1.5. For tasks involving semantic segmentation, this model per-
forms exceptionally well. The encoder is comprised of two consecutive 3 × 3 con-
volutions (conv) with zero padding and a rectied linear unit (ReLU) (Agarap 2019)
activation function. To connect levels or perform down sampling, a max- pooling
operation with stride 2 is employed. At every level after that, the channel number of
feature maps is doubled. A 2 × 2 up- convolution (up- conv) is used in the symmetric
decoder counterpart in addition to up sampling, in order to cut the number of chan-
nels in half. To maintain the low- level information, the center- cropped feature map
from the encoder is sent via skip connections to the decoder at each level. For con-
catenation, cropping is required to keep feature maps of the same size. We then apply
ReLU and two more 3 × 3 convolutions. The channel number is nally converted to
the required number of classes C using a 1 × 1 convolution. The network creates a
segmentation map with C classes in this conguration based on a 2D image as input.
Segmentations performed by U- Net are shown in Figure 1.6.
FIGURE 1.5 U-Net Architecture.
10 Impact of Articial Intelligence on Society
1.3.1.1 Advantages of U-Net over Traditional Image Segmentation Methods
1. Excellent Precision and Adaptability: U- Net has proven to be highly
accurate and performs at the cutting edge in a variety of image segmenta-
tion tasks, especially in the analysis of medical images. It is a exible option
for various applications because it can be adjusted to a broad variety of
segmentation tasks.
2. End- to- End Learning: Feature descriptions are directly learned by U- Net
from the input data. It can adjust to the unique properties of the data because
it doesn’t rely on manually constructed feature engineering.
3. Sturdy against Variability: Conventional techniques frequently depend on
manually adjusted parameters and presumptions about the properties of the
FIGURE 1.6 Results of U-Net-based segmentation (a) and (c) are original brain tumor and
skin cancer images, (b) and (d) are segmented results of brain and skin cancer images.
Disruptive Innovation in Medical Image Segmentation 11
images, which might not always be accurate. Because U- Net is data- driven,
it is more resistant to changes in the data.
4. Retrieves Local and Contextual Data: Through its encoder- decoder
architecture, U- Net is able to capture both local and contextual information,
which facilitates precise and context- aware segmentations.
5. Maintaining Spatial Information: Fine- grained spatial information is
preserved in many segmentation tasks by U- Net’s use of skip connections
between the encoder and decoder layers.
6. Decreased Manual Annotation Work: Conventional segmentation tech-
niques frequently necessitate laborious manual feature engineering and
training annotation efforts. Through transfer learning, U- Net can lessen
the need for manually created features and, in certain situations, the effort
required for annotation.
7. Automation: Utilizing U- Net can reduce the need for human operators to
complete labor- intensive and complex segmentation tasks by enabling fully
automated segmentation tasks.
8. Progressive Training: The network can learn gradually from coarse fea-
tures to ne details due to U- Net’s encoder- decoder architecture, which is
crucial for tasks like medical image segmentation.
9. Ability to Scale and Adapt: Widely applicable to a variety of image seg-
mentation problems, U- Net models can be trained on large datasets and
exhibit good generalization to new data.
In complex and varied segmentation tasks in particular, U- Net proves benecial
when high accuracy, generalization, and automation are needed. In situations where
interpretability, computational efciency, and domain- specic knowledge are criti-
cal, traditional approaches might still be useful. The specic requirements of the
application, the availability of data, and the available computational resources all
inuence the decision between U- Net and more conventional approaches. In order
to get the best of both worlds, hybrid approaches—which combine the advantages of
both U- Net and conventional methods—are frequently investigated.
1.3.2 GeneRative adveRsaRial netwoRk
In 2014, Ian Goodfellow and associates developed a class of machine learning algo-
rithms called Generative Adversarial Networks or GANs (Goodfellow et al. 2014).
The purpose of GANs is to create fresh, never- before- seen data that closely resem-
bles a given set of training data. A generator and a discriminator are two neural
networks that interact in the fundamental idea of GANs. Figure 1.7 shows the overall
architecture of GAN. The generator is a neural network that attempts to produce data
samples (such as images) that resemble the training data by using random noise or
random input as its input.
The generator generates random noise at rst, and the samples it creates are usu-
ally meaningless and dissimilar to the actual data. An additional neural network that
assesses the veracity of a particular data sample is called the discriminator. It distin-
guishes between real and false input by using samples of created data from the
12 Impact of Articial Intelligence on Society
generator and real data samples from the training set. The discriminator is taught to
discern between authentic and counterfeit samples. Since the generated samples are
random and don’t accurately reect the real data, it performs poorly at rst.
These two networks engage in competitive interaction throughout the GAN’s
training phase.
a) Training the Discriminator
The discriminator is trained using samples of created data from the genera-
tor labeled “fake” and real data samples labeled “real.” The objective of the
discriminator is to accurately identify phony and authentic samples. With
time, it learns to become more accurate.
b) Training the Generator
The generator creates samples by using random noise as input. The dis-
criminator receives these created samples, and the generator’s goal is to
create samples that it deems to be “real.” Enhancing the generator’s capacity
to produce samples that are identical to genuine data is the goal.
c) Adversarial Training
In an adversarial way, the discriminator and generator are trained concur-
rently. The discriminator improves its ability to discern between authentic
and phony samples as the generator becomes more adept at producing real-
istic samples. Both networks continuously improve their performance as a
result of this adversarial process.
In the context of image or data segmentation, a GAN is used to create segmented out-
put from an input image or set of data. The generator, or part of the GAN, creates the
segmented images, and the discriminator, or other part of the GAN, assesses the pre-
cision and quality of the segmentation. The segmentation results are more accurate
and of higher quality thanks to this adversarial approach. GANs have been applied
to a variety of segmentation tasks, including object segmentation in computer vision
and medical image segmentation.
The training process keeps going until a predetermined threshold is reached, like
a predetermined number of iterations or the generated samples attain the required
FIGURE 1.7 GAN Architecture.
Disruptive Innovation in Medical Image Segmentation 13
level of realism. In a perfect world, the discriminator is unable to consistently discern
between created and genuine samples, and the generator learns to produce examples
that are comparable to the training data. The segmented results obtained by GAN
network on brain and skin cancer images are shown in Figure 1.8.
1.3.2.1 Advantages of GAN over Traditional Methods
Regarding circumstances like uncommon environmental conditions or rare medi-
cal disorders, when real data collection is challenging, GANs can be used to create
synthetic data. To help segmentation models handle boundary instances, this can be
used during training. To improve the delineation of object borders, GANs can be
employed in conjunction with conventional segmentation approaches. In order to
enable segmentation models provide more accurate and precise ndings, GANs can
produce images with more clear object boundaries. High- level semantic features can
FIGURE 1.8 Results of GAN-based segmentation: (a) and (c) are original brain tumor and
skin cancer images, (b) and (d) are segmented results of brain and skin cancer images.
14 Impact of Articial Intelligence on Society
be learned by GANs from the data. Semantic information can be added to the seg-
mentation process by employing GANs in a multi- modal manner, which improves
the segmentation outcomes.
1.4 EVALUATION METRICS FOR IMAGE SEGMENTATION
a) Jaccard Index
A metric for comparing two sets’ similarity is the Jaccard Index, sometimes referred
to as the Jaccard Similarity Coefcient (Everingham et al. 2015). It is frequently uti-
lized in many different domains, including data mining, natural language processing,
and information retrieval. By comparing the intersection and union of two sets, the
Jaccard Index calculates how similar the two sets are to one another. It is also known
as Intersection over Union (IoU).
Equation 1.3 can be used to determine the Jaccard Index:
Jaccard Index (J) = (Size of Intersection of Sets A and B)/(Size of Union of Sets
A and B)
o
rJacquard Index, =
ST
ST
∩
∪
(1.3)
where T is the ground truth data and S is the anticipated segmentation data. A result
between 0 and 1, where 0 indicates no segmentation and 1 indicates an accurate seg-
mentation, is what Jaccard Index returns.
b) Dice Score
The DICE score (Martin, Fowlkes, and Malik 2004), which is sometimes referred
to as the Sørensen–Dice coefcient or Dice similarity coefcient, is a frequently
employed metric in image segmentation that is used to assess the precision of a
segmentation technique or model quantitatively. It calculates how close an image’s
expected and ground truth segmentations are to each other. The DICE score is a
number between 0 and 1, where 0 denotes no overlap and 1 denotes a perfect match
between the expected and ground truth segmentations. Equation 1.4 denotes the
mathematical expression for dice score:
Dice Scor
e
2ST
ST
(1.4)
In order to provide a balanced measure of segmentation quality, the DICE score
considers both the true positive (properly segmented) and false positive (over-
segmented) and false negative (under- segmented) regions. This makes it a useful
metric in the evaluation of image segmentation. Greater accuracy in segmentation is
indicated by higher DICE values, while lower scores imply less accurate segmenta-
tions. To thoroughly evaluate the effectiveness of segmentation models, it is one of
several metrics that are combined with others, such as Intersection over Union.
Disruptive Innovation in Medical Image Segmentation 15
c) Precision
The percentage of correctly categorized positive pixels (such as accurately seg-
mented object pixels) among all the positive pixels predicted by the model is referred
to as precision in image segmentation (Villmann et al. 2014). It gauges how well the
model predicts things. Precision can be expressed by Equation 1.5:
Precision
TP
TP FP
(1.5)
d) Recall
The ratio of true positive pixels to all actual positive pixels in the ground truth seg-
mentation is known as recall in image segmentation (Taha and Hanbury 2015). It
gauges how well the model can recognize every real positive pixel. Equation 1.6
mathematically expresses the recall metric:
Recall
TP
TP FN
(1.6)
False positives (FP) are pixels that are incorrectly classied as being a part of the
target item, whereas true positives (TP) are pixels that are accurately identied as
belonging to the target object. Pixels in the ground truth that the model failed to
detect are known as false negatives (FN).
1.5 ADVANCED AI (DEEP LEARNING) AS A DISRUPTIVE
TECHNOLOGY IN MEDICAL IMAGE SEGMENTATION
Medical image segmentation has found deep learning to be a very disruptive tech-
nique. Medical image segmentation, which entails locating and dening anatomical
structures, tumors, lesions, and other regions of interest within medical images like
CT scans, MRIs, X- rays, and histopathology slides, is an essential task in health-
care and clinical diagnosis. Diagnostic image segmentation has shown impressive
accuracy when using deep learning models, particularly convolutional neural net-
works (CNNs) and their derivatives. They can reliably and automatically identify
and segment structures that, because of minute uctuations or noise in the images,
could be difcult for human experts to identify. Semantic segmentation (classifying
structures into groups such as organs or tumors) and instance segmentation (identi-
fying distinct instances of the same structure) are made possible by deep learning
models (Avendi, Kheradvar, and Jafarkhani 2015). This is very helpful for tracing
the evolution of particular nodules over time or measuring the number of lesions.
Models with relatively small labeled datasets can be adapted to new medical image
segmentation tasks through transfer learning and pre- trained models. This lessens the
need for manual annotation, which is expensive and time- consuming. Through the
16 Impact of Articial Intelligence on Society
identication of tiny abnormalities in medical imaging, deep learning models can aid
in the early detection of diseases like cancer or neurodegenerative disorders (Bernard
et al. 2018). This could lead to early treatments and better patient outcomes. Deep
learning models can be tailored to certain patient demographics and medical situa-
tions. This exibility is especially helpful in areas with varying requirements and
standards, such as cardiology, cancer, and radiology.
Deep learning has made a strong technological statement over more conventional
approaches in a variety of elds, including computer vision, natural language pro-
cessing, and medical imaging. Feature learning is one of its main advantages since
deep learning models can automatically extract complex features from data, elimi-
nating the need for human feature engineering (Luo et al. 2020). Not only is this
automatic feature learning effective, but it also improves generalization over a vari-
ety of datasets. The scalability and adaptability of deep learning are also attractive
features. Large data volumes and processing resources can be easily accommodated
by it, which makes it appropriate for tasks involving substantial data growth. Deep
learning models are remarkably exible because they can be tailored for many
applications using a same architecture, like a recurrent neural network (RNN) or
convolutional neural network (CNN), which eliminates the need for task- specic
methods.
Deep learning has some challenges despite its amazing benets. For training, it
usually requires large volumes of labeled data, which might not be available for
every task. Large amounts of calculation may be required, requiring expensive, pow-
erful technology. Deep learning models’ interpretability is a persistent problem, par-
ticularly in delicate industries like nance and healthcare. One potential issue that
calls for the use of regularization approaches is overtting. When implementing deep
learning, ethical issues with prejudice, justice, and privacy must also be properly
addressed. In general, although deep learning has notable benets over conventional
techniques, its implementation must be approached mindful of its constraints and
moral ramications. It’s a powerful technology that can spur innovation in many dif-
ferent businesses and transform many other elds when used carefully.
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