Machine Learning-Based Medical Imaging Detection and Diagnostic Assistance

Abstract

With the development of medical technology, medical imaging technology plays an increasingly important role in diagnosing and monitoring diseases. At the same time, machine learning technology has shown strong capabilities in data processing and analysis, providing new possibilities for the detection and auxiliary diagnosis of medical images. This paper will deeply discuss the application of machine learning in medical images, especially the application of deep learning in medical images, and how to achieve effective medical image detection and auxiliary diagnosis through steps such as preprocessing, feature extraction, model training and optimization.

Full text

International Journal of Computer Science and Information Technology
ISSN: 3005-9682 (Print), ISSN: 3005-7140 (Online) | Volume 2, Number 1, Year 2024
DOI: https://doi.org/10.62051/ijcsit.v2n1.05
Journal homepage: https://wepub.org/index.php/IJCSIT/index
Content from this work may be used under the terms of CC BY-NC 4.0 licence (https://creativecommons.org/licenses/by-nc/4.0/).
Published by Warwick Evans Publishing.
WEP
Warw ick
Evans
Publishing
Machine Learning-Based Medical Imaging Detection and
Diagnostic Assistance
Qiang Zeng
1, *
, Wenjian Sun
2
, Jingyu Xu
3
, Weixiang Wan
4
, Linying Pan
5
1Computer Technology, Zhejiang University, Hangzhou, Zhejiang, China;
2Electronic and information engineering, Yantai University, ShanDong, China;
3Computer Information Technology, Northern Arizona University, Flagstaff, Arizona, USA;
4Electronics & Communication Engineering, University of Electronic Science and Technology of
China, Chengdu, China;
5Information studies, Trine University, Phoenix, Arizona, USA.
*Corresponding Author: qiangz629@gmail.com
ABSTRACT
With the development of medical technology, medical imaging technology plays an increasingl
y
important role in diagnosing and monitoring diseases. At the same time, machine learning
technology has shown strong capabilities in data processing and analysis, providing new possibilities
for the detection and auxiliary diagnosis of medical images. This paper will deeply discuss the
application of machine learning in medical images, especially the application of deep learning in
medical images, and how to achieve effective medical image detection and auxiliary diagnosis
through steps such as preprocessing, feature extraction, model training and optimization.
KEYWORDS
Ai-assisted Detection of Medical Images; Convolutional Neural Networks; Applications.
1. INTRODUCTION
Medical imaging technologies, such as X-rays, CT and MRI, provide doctors with intuitive and
accurate diagnostic information. However, due to the large amount and diverse forms of medical
image data, traditional image analysis methods are often difficult to extract key information
accurately and efficiently. In recent years, with the development of machine learning technology,
especially deep learning technology, a new solution has been provided for automatic detection and
auxiliary diagnosis of medical images[1-4].
Ai-assisted detection of medical images refers to the use of artificial intelligence technology to assist
the analysis and diagnosis of medical images.[3] Medical image is a very important source of medical
information, through which we can observe the internal structure, lesions and abnormalities of the
human body. However, the analysis of medical images is a very complex and time-consuming task
that requires a doctor's extensive expertise and experience. Ai-assisted Detection medical image
technology can help doctors quickly and accurately diagnose diseases by automatically identifying
and analyzing medical images.
Ai-assisted detection medical imaging technology can be applied to different medical fields, including
radiology, ultrasound, endoscopy and so on. For example, in the field of radiology, AI can assist
doctors in analyzing images such as X-rays, CT and MRI to help doctors quickly and accurately

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diagnose diseases such as fractures and tumors.[4] In the field of ultrasound, AI can assist doctors to
analyze ultrasound images and help doctors more accurately determine whether the fetus, heart, blood
vessels and other organs are normal or not. In the field of endoscopy, AI can assist doctors to analyze
endoscopic images and help doctors quickly and accurately diagnose digestive tract diseases.
2. RELATED WORK
2.1. Overview CT
CT (electronic computed tomography) is a kind of clinical diagnostic equipment.[5-7] It uses
precisely collimated X-ray beams, gamma rays, ultrasonic waves, etc., together with highly sensitive
detectors around a certain part of the human body for one section scanning one by one, with fast
scanning time, clear images and other characteristics. [8]According to the different rays used, it can
be divided into: X-ray CT (X-CT) and γ-CT (γ-CT).
CT is a kind of equipment integrated by X-ray diagnosis and computer data processing technology,
which can examine all organic diseases, especially the organic space occupying lesions with large
density differences, and can make qualitative diagnosis. [9]The most suitable for CT examination is
brain diseases, which have the best effect on tumor, hemorrhage and infarction, head and neck
diseases, chest diseases, abdominal and pelvic parenchymal viscera diseases, bone and joint diseases
can also have a better diagnostic effect.
CT machine belongs to the radioactive inspection equipment, the subject will have a certain radiation
exposure. At the same time, MRI, also known as nuclear magnetic resonance imaging, is a
biomagnetic spin imaging technology.[10-12] Due to its imaging pixels several times more than CT
and high soft tissue resolution, its contrast for soft tissue examination is significantly higher than CT.
MRI examination is non-invasive and does not contain ionizing radiation exposure, avoiding damage
to the human body. However, MRI is expensive and difficult to widely carry out.
2.2. The combination of CT and AI
The combination of AI and CT is mainly reflected in medical image diagnosis. By applying AI
technology to CT images, case screening, intelligent analysis and diagnosis, and auxiliary clinical
diagnosis and treatment decision-making can be completed more accurately and quickly.[13] This
combination has a variety of product forms, including image analysis and diagnosis software, CT
image 3D reconstruction system, automatic target delineation and adaptive radiotherapy system.
AI+CT image recognition technology has a wide range of application scenarios, including the chest,
limbs, joints and other parts, as well as organs and tissues such as breast, heart and lung, coronary
arteries and bones. Compared with manual reading, the advantage of AI reading is its high efficiency
and low cost, and with the maturity of product technology, the accuracy of AI reading will gradually
improve.

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Figure 1: Analysis of AI-assisted diagnosis software for CT images of pneumonia
Intelligent analysis of CT images through AI technology can provide doctors with more objective and
accurate diagnosis basis, thereby improving the efficiency and accuracy of diagnosis and treatment.
[14]At the same time, AI technology can also help doctors quickly locate the lesion site and reduce
missed diagnosis and misdiagnosis. In the future, with the continuous development and improvement
of AI technology, the application of AI+CT imaging technology will be more extensive, providing
more help and support for medical diagnosis and treatment.
2.3. Implementation and application of CT in convolutional network model
Convolutional Neural Networks (CNNS) are one of the most important models in deep learning,
mainly used for image recognition and computer vision tasks. The main feature of the CNN model is
the use of Convolution, which allows the model to learn features on the local space.[15-17]
Convolutional neural networks (CNNS) are widely used in image recognition and processing. For CT
images, convolutional neural networks can effectively extract the features in the images, and provide
support for subsequent tasks such as classification and diagnosis. There are many applications of
convolutional neural networks in CT images, such as feature extraction. Convolutional neural
networks can automatically learn features in images through multi-layer convolution and pooling
operations.[18] For CT images, CNN can extract the location, size, shape and other information of
the lesion to provide a basis for the diagnosis of the disease. The automatic diagnosis system based
on convolutional neural network can quickly analyze a large number of CT images and improve the
diagnosis efficiency. Through the classification and recognition of CT images, automatic and
intelligent diagnosis can be realized. Convolutional neural networks can also be used as adjuvant
therapy. By analyzing CT images, doctors can more accurately determine the location and extent of
the lesion, and thus develop a more precise treatment plan. During the treatment process,
convolutional neural networks can analyze the CT images of patients and evaluate the treatment effect.
By comparing the CT images before and after treatment, it is possible to determine whether the
treatment plan is effective. Convolutional neural networks can also be used to generate medical
images. By generating high-quality CT images, more accurate diagnostic information can be provided,
which is helpful to improve doctors' ability to diagnose diseases.
In conclusion, convolutional neural networks are playing an increasingly important role in CT image
processing and diagnosis.[19-20] With the continuous development of technology, convolutional
neural networks will bring more innovations and breakthroughs to the field of medical imaging.

39
Figure 2: Operation interface of the novel coronavirus pneumonia CT image AI screening system
3. METHODOLOGY
Topic: Detection and processing of pulmonary nodules in CT images based on convolutional neural
network
3.1. Experimental model
Convolutional Neural Network (CNN) : In this experiment, we adopted a simple convolutional neural
network structure for pulmonary nodule detection. [21]The network consists of a convolutional layer,
a pooled layer, and a fully connected layer, and is used to learn features from CT images and perform
binary classification (nodule/non-nodule).
Figure 3: Network structure diagram
3.2. Experimental process
Data preparation: CT scan images and corresponding labels are obtained using a publicly available
medical image dataset, such as LUNA16, where labels indicate the location and size of nodules.[22-
24]
Data preprocessing: CT images are preprocessed, including grayscale, normalization and cropping,
to improve the effect and speed of network training.

40
Building Convolutional neural network models: Build convolutional neural network models using
Python and deep learning frameworks such as TensorFlow or PyTorch.
Network training: The pre-processed CT images are input into the convolutional neural network to
train the model. Optimize the model parameters by minimizing the loss function.
Model evaluation: The trained model is evaluated using a separate set of reserved data to calculate
the model's accuracy, recall rate and F1 score.
3.3. Experimental algorithm
Convolution operation: The convolution layer is used to extract features from the image.[25-27] In
the convolution operation, the sliding convolution kernel (filter) is used to carry out the convolution
operation on the image, and the local features of the image are extracted.
Formula:
(1)
Where Kernel represents the convolution kernel, Input represents the input image, and b represents
the offset item.
Pooling operation: The pooling layer is used to reduce the size of the feature map while preserving
the main features.
Formula (maximum pooling) :
(2)
Experimental results
LUNA16 data set was used for training and testing, and experimental results were obtained as follows:
Accuracy: 90% Recall rate: 85% F1 score: 0.87
Figure 4: Loss diagram of U-net

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Figure 5: Acc plot on training set
3.4. Experimental conclusion
In this experiment, convolutional neural networks were used to detect and process lung nodules in
CT images successfully.
The constructed convolutional neural network model performs well on the test set with high accuracy
and recall rate.[28]
In the future, the network structure and parameters can be further optimized to improve the
performance and generalization ability of the model.
The difference between AI and traditional medical image detection in the medical industry
In terms of detection accuracy, traditional medical image detection relies on the experience and skills
of doctors. Due to the different subjective factors and technical level of human beings, the accuracy
of diagnosis is quite different. Through machine learning and deep learning algorithms, AI technology
can understand and obtain the most valuable features in a large number of image data to assist doctors
to correctly evaluate and predict the disease and improve the accuracy of diagnosis.[29-30] Studies
have shown that AI has reached or even surpassed the level of human doctors in the diagnosis of
certain diseases.
There are also certain differences in detection efficiency. Traditional medical image detection
requires doctors to spend a lot of time on image analysis and diagnosis, while AI technology can
quickly analyze medical images and improve diagnostic efficiency.
Figure 6: Auxiliary screening for cervical cancer

42
In terms of report standardization, the writing of traditional medical image test report usually involves
the subjective factors of individual doctors and the requirements of various hospitals, which leads to
the differences in the style and content of the report. AI technology can generate unified and
standardized diagnostic reports through standardized algorithms and models to provide consistent
results. Traditional medical imaging tests often cannot be personalized according to the individual
differences and needs of patients. AI technology can realize personalized medical image
interpretation according to the specific situation and needs of patients.
In general, AI has the advantages of higher accuracy, efficiency, reporting standardization and
personalized interpretation in medical image detection, which can effectively solve the problems
existing in traditional medical image detection.
4. CONCLUSION
Machine learning-based medical image detection and assisted diagnosis is a technology with potential
to help doctors diagnose diseases more accurately and improve the efficiency of medical diagnosis.
Diagnostic accuracy can be improved, and machine learning algorithms can learn features from large
amounts of medical image data and can assist doctors in identifying signs of disease.[31] Compared
to traditional manual examinations, machine learning algorithms can provide more accurate diagnosis
results in some cases. It can also reduce the rate of misdiagnosis, and machine learning algorithms
can help reduce the rate of misdiagnosis in medical image detection, especially for some more
complex or confusing diseases. By training a large amount of data, the algorithm can identify small
feature differences and reduce the possibility of misdiagnosis.
To improve the speed of diagnosis, machine learning algorithms can quickly process large amounts
of medical image data, which can complete the diagnosis process more quickly than manual
examination. This is especially important for diseases that require urgent diagnosis, such as lung
nodules and stroke. To help doctors aid decision-making, machine learning algorithms can act as an
aid to doctors, providing second opinions or reference opinions. Doctors can combine the results of
machine learning algorithms with their own clinical experience to make more accurate diagnosis and
treatment decisions. It is very helpful for patient data privacy and security, and the privacy and
security of medical data need to be considered when applying machine learning algorithms. Ensuring
the secure storage and transmission of medical data, as well as compliance with relevant laws and
regulations, is an important measure to safeguard patient privacy.
In general, medical image detection and auxiliary diagnosis technology based on machine learning
has played a huge role in the medical field. In the aspects of lesion identification, image segmentation,
quantitative analysis, diagnostic assistance, prognosis assessment and image quality control, these
technologies provide a more accurate and efficient method, which helps to improve the quality of
medical services and improve the quality of life of patients.[32] In the future, with the continuous
progress of machine learning technology, its application in the medical field will be more extensive
and in-depth, opening up new possibilities for the development of medical services.
ACKNOWLEDGEMENT
I would like to sincerely thank Professor Huiying Weng for his outstanding contributions and
profound insights in the field of machine learning. His literature and core theories provide valuable
theoretical support and inspiration for the research of this paper. In particular, I would like to thank
him for his article "The Importance of AI Algorithm Combined with Tunable LCST Smart Polymers
in Biomedical "Applications" provides important reference materials for this research, and provides
key guidance for the theoretical framework and practical application of my paper. In addition, I would
like to thank Professor Huiying Weng for his guidance and support in academic exchange and

43
research cooperation. Thank you for your valuable advice and encouragement, which have played an
immeasurable role in promoting my research work. Finally, I would like to express my sincere thanks
and high respect to Professor Huiying Weng again, and look forward to more opportunities for
academic exchanges and cooperation in the future.
REFERENCES
[1] Akbar, A., Peoples, N., Xie, H., Sergot, P., Hussein, H., Peacock IV, W. F., & Rafique, Z. . (2022). Thrombolytic
Administration for Acute Ischemic Stroke: What Processes can be Optimized?. McGill Journal of Medicine, 20(2).
[2] Chen, Wangmei, et al. “Applying Machine Learning Algorithm to Optimize Personalized Education
Recommendation System”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Feb. 2024, pp.
101-8, doi:10.53469/jtpes.2024.04(01).14.
[3] “Implementation of Computer Vision Technology Based on Artificial Intelligence for Medical Image Analysis”.
International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 69-76,
https://doi.org/10.62051/ijcsit.v1n1.10.
[4] Dong, Xinqi, et al. “The Prediction Trend of Enterprise Financial Risk Based on Machine Learning ARIMA Model”.
Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 65-71,
doi:10.53469/jtpes.2024.04(01).09.
[5] “A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”.
International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92,
https://doi.org/10.62051/ijcsit.v1n1.12.
[6] K. Jin, Z. Z. Zhong and E. Y. Zhao, "Sustainable Digital Marketing Under Big Data: An AI Random Forest Model
Approach," in IEEE Transactions on Engineering Management, vol. 71, pp. 3566-3579, 2024, doi:
10.1109/TEM.2023.3348991.
[7] Jin, Keyan. "Impacts of Word of Mouth (WOM) on E-Business Online Pricing." JGIM vol.31, no.3 2023: pp.1-17.
http://doi.org/10.4018/JGIM.324813
[8] Du, S., Li, L., Wang, Y., Liu, Y., & Pan, Y. (2023). Application of HPV-16 in Liquid-Based thin Layer Cytology
of Host Genetic Lesions Based on AI Diagnostic Technology Presentation of Liquid. Journal of Theory and Practice
of Engineering Science, 3(12), 1-6.
[9] Xin, Q., He, Y., Pan, Y., Wang, Y., & Du, S. (2023). The implementation of an AI-driven advertising push system
based on a NLP algorithm. International Journal of Computer Science and Information Technology, 1(1), 30-37.
[10] He, Yuhang, et al. “Intelligent Fault Analysis With AIOps Technology”. Journal of Theory and Practice of
Engineering Science, vol. 4, no. 01, Feb. 2024, pp. 94-100, doi:10.53469/jtpes.2024.04(01).13.
[11] Chen , J., Xiong, J., Wang, Y., Xin, Q., & Zhou, H. (2024). Implementation of an AI-based MRD Evaluation and
Prediction Model for Multiple Myeloma. Frontiers in Computing and Intelligent Systems, 6(3), 127-131.
https://doi.org/10.54097/zJ4MnbWW
[12] Tan, Kai, et al. “Integrating Advanced Computer Vision and AI Algorithms for Autonomous Driving Systems”.
Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 41-48,
doi:10.53469/jtpes.2024.04(01).06.
[13] “Exploring New Frontiers of Deep Learning in Legal Practice: A Case Study of Large Language Models”.
International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 131-8,
https://doi.org/10.62051/ijcsit.v1n1.18.
[14] Development of Machine Learning and Artificial Intelligence in Toxic Pathology. (2024). Frontiers in Computing
and Intelligent Systems, 6(3), 137-141. https://doi.org/10.54097/Be1ExjZa
[15] Jili Qian, et al. “Analysis and Diagnosis of Hemolytic Specimens by AU5800 Biochemical Analyzer Combined
With AI Technology”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 100-3,
https://doi.org/10.54097/qoseeQ5N.
[16] “A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”.
International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92,
https://doi.org/10.62051/ijcsit.v1n1.12.
[17] Pan, Yiming, et al. “Application of Three-Dimensional Coding Network in Screening and Diagnosis of Cervical
Precancerous Lesions”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 61-64,
https://doi.org/10.54097/mi3VM0yB.
[18] Wei, Kuo, et al. “Strategic Application of AI Intelligent Algorithm in Network Threat Detection and Defense”.
Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 49-57,
doi:10.53469/jtpes.2024.04(01).07.

44
[19] Chen, Wangmei, et al. “Applying Machine Learning Algorithm to Optimize Personalized Education
Recommendation System”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Feb. 2024, pp.
101-8, doi:10.53469/jtpes.2024.04(01).14.
[20] “Implementation of Computer Vision Technology Based on Artificial Intelligence for Medical Image Analysis”.
International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 69-76,
https://doi.org/10.62051/ijcsit.v1n1.10.
[21] Dong, Xinqi, et al. “The Prediction Trend of Enterprise Financial Risk Based on Machine Learning ARIMA Model”.
Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 65-71,
doi:10.53469/jtpes.2024.04(01).09.
[22] “A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”.
International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92,
https://doi.org/10.62051/ijcsit.v1n1.12.
[23] “Unveiling the Future Navigating Next-Generation AI Frontiers and Innovations in Application”. International
Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 147-56,
https://doi.org/10.62051/ijcsit.v1n1.20.
[24] Zong, Yanqi, et al. “Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset”.
Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 29-35,
doi:10.53469/jtpes.2023.03(12).05.
[25] An Overview of the Development of Stereotactic Body Radiation Therapy. (2024). Frontiers in Computing and
Intelligent Systems, 6(3), 56-60. https://doi.org/10.54097/09nIy12x.
[26] Zheng, Jiajian, et al. “The Credit Card Anti-Fraud Detection Model in the Context of Dynamic Integration Selection
Algorithm”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 119-22,
https://doi.org/10.54097/a5jafgdv.
[27] Wang, Sihao, et al. “Diabetes Risk Analysis Based on Machine Learning LASSO Regression Model”. Journal of
Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 58-64, doi:10.53469/jtpes.2024.04(01).08.
[28] “Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms”.
International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 60-68,
https://doi.org/10.62051/ijcsit.v1n1.09.
[29] Yu, L., Liu, B., Lin, Q., Zhao, X., & Che, C. (2024). Semantic Similarity Matching for Patent Documents Using
Ensemble BERT-related Model and Novel Text Processing Method. arXiv preprint arXiv:2401.06782.
[30] Huang, J., Zhao, X., Che, C., Lin, Q., & Liu, B. (2024). Enhancing Essay Scoring with Adversarial Weights
Perturbation and Metric-specific AttentionPooling. arXiv preprint arXiv:2401.05433.
[31] Du, Shuqian, et al. “Application of HPV-16 in Liquid-Based Thin Layer Cytology of Host Genetic Lesions Based
on AI Diagnostic Technology Presentation of Liquid”. Journal of Theory and Practice of Engineering Science, vol.
3, no. 12, Dec. 2023, pp. 1-6, doi:10.53469/jtpes.2023.03(12).01.
[32] Pan, Yiming, et al. “Application of Three-Dimensional Coding Network in Screening and Diagnosis of Cervical
Precancerous Lesions”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 61-64,
https://doi.org/10.54097/mi3VM0yB.