TUBERCULOSIS DIAGNOSIS USING X-RAY IMAGES.

Full text

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
689
Journal Homepage: -www.journalijar.com
Article DOI:10.21474/IJAR01/8872
DOI URL: http://dx.doi.org/10.21474/IJAR01/8872
RESEARCH ARTICLE
TUBERCULOSIS DIAGNOSIS USING X-RAY IMAGES.
Saad Akbar1, Najmi Ghani Haider2 And Humera Tariq3.
1. Postgraduate Student, NED University of Engineering & Technology, Karachi, Pakistan.
2. Professor, Department of Computer Science & Information Technology, NED University of Engineering &
Technology, Karachi, Pakistan.
3. Assistant Professor, Department of Computer Science, University of Karachi, Karachi, Pakistan.
……………………………………………………………………………………………………....
Manuscript Info Abstract
……………………. ………………………………………………………………
Manuscript History
Received: 10 February 2019
Final Accepted: 12 March 2019
Published: April 2019
Key words:-
Chest X-ray, TB image classification,
convolutional neural networks,
supervised learning algorithms.
Tuberculosis (TB) is caused by the bacteria Mycobacterium
tuberculosis. It most often affects the lungs. Tuberculosis is a
preventable and curable disease. The Global Annual TB report, 1.5
million TB related deaths were reported in 2015. In 2016, this increased
with 1.7 million reported deaths and more than 10 million people
infected with the disease. The objective of this work is to analyze
medical X-ray images using deep learning methods and explore images
to achieve classification of Tuberculosis. The Convolutional Neural
Networks (CNN) algorithm based deep learning classification
approaches has been chosen as it has the ability to intrinsically extract
the low level representations from data using little pre-processing in
comparison with other image classification algorithms. This simple and
efficient model will lead clinicians towards better diagnostic decisions
for patients to provide them solutions with good accuracy for medical
imaging. Supervised learning algorithms convolutional neural networks
(CNN) were considered for the classification task. The performance of
the designed model is measured on two publicly available datasets: the
Montgomery County chest X-ray (MC) and Shenzhen chest X-ray set.
It achieves accuracy of 90% and 80% respectively on these datasets.
Copy Right, IJAR, 2019,. All rights reserved.
……………………………………………………………………………………………………....
Introduction:-
Tuberculosis (TB) is an infectious disease affecting populations all over the world and is commonly caused by
bacteria known as Mycobacterium tuberculosis, and mostly affecting the lungs of human beings. However, it can
also affect other organs. TB is spread through the air by infected persons, by coughing, sneezing, spitting, as well as
eating utensils used by patients. The TB bacteria spread widely through the air. One third of population of the world
every year gets Mycobacterium TB bacteria at a rate of one percent of population with new infection [1]. TB is
among one of the top 10 causes of death and the leading cause from a single infectious agent, which is above
HIV/AIDS. TB causes millions of people to fall ill each year. TB caused an estimated 1.3 million deaths (range,
1.2–1.4 million) among HIV-negative people and there were an additional 300,000 deaths from TB (range 266,000–
335,000) among HIV-positive people in 2017 [1].
Corresponding Author:-Saad Akbar.
Address:-Postgraduate Student, NED University of Engineering & Technology, Karachi, Pakistan.

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
690
According to WHO report, in 2017, globally 10 million people (between 9 – 11 million) experienced TB disease in
which there were 5.8 million men, 3.2 million women and 1.0 million children. There were cases in all countries, but
two thirds were in eight countries: Pakistan, India, Indonesia, China, the Philippines, Nigeria, South Africa and
Bangladesh, and in all age groups, but overall 90% were adults (aged ≥15 years), 9% were people living with HIV
(72% in Africa) [1]. For many countries, however, the ―end‖ of TB as an epidemic and major public health problem
is still a distant reality. This is despite the fact that, with a timely diagnosis and correct drug treatment, most people
who develop the disease can be cured. Twenty–five years ago, in 1993, WHO declared TB a global health
emergency.
The studies done on human skeletons have found that it has been affecting humans for thousands of years. The cause
remained unknown to science before 24 March 1882, when Dr. Robert Koch declared that he had found the bacillus
Mycobacterium tuberculosis, an event that is now commemorated each year as World TB Day [2]. The disease is
extended when the infected people with TB disperse bacteria into the atmosphere, for example by sneezing,
coughing. TB was one of the leading causes of death in the late 1800s in some European countries according to the
cause-of-death data from national vital registration system. But with a number of TB victims started to decline in
North America, Europe and few other countries around the globe in the 20th century. From the 1940s, drug
treatments’ discovery, development and their use significantly accelerated these trends, with national TB case rates
((per 100,000) population) decreasing to about 10% per year death rates declining even faster. TB is considered as a
disease of the past in countries that have only around 10 or less cases and less than 1 death per 100,000 populations
each year.
The aim of this work is the classification of healthy and unhealthy lungs based on chest radiographs only using
Convolutional Neural Network (CNN). The framework should be able to classify pulmonary Tuberculosis
depending on the available data for training. In order to achieve the best possible results, machine learning
frameworks were applied and evaluated. The classification of radiographs was chosen because it is the most
commonly used type of medical imaging and it is the first step in Tuberculosis detection due to its quick availability.
To give an example, 54% of all diagnostic images made from March 2016 to March 2017 in England were
radiographs [3]. Therefore a lot of data exists that could be used to train and test the resulting frameworks. This
framework should support doctors in classification and to make their workflow faster. This work exploits the
convergence of imaging research and system to advance the knowledge in automated CXR image analysis by
automatically detecting presence of pulmonary tuberculosis in digital CXRs, leading to suitable discrimination for
screening, as well as to compute a measure of confidence in its determination.
Literature Review:-
In this study [4] state-of-the-art CADx software makes use of machine learning (ML) techniques that use global and
local feature descriptors to extract features from the underlying data. Previously, ML tools have been used to detect
abnormal texture in chest radiographs and to exhibit extraction of texture and shape features and classification with a
binary classifier in the process of TB screening from CXRs. Algorithms based on morphology have been put forth to
extract features including circularity, size, contrast and local curvature of the lung nodules for classification of
abnormal and normal CXRs. Machine learning (ML) techniques are used by the state of the art software CADx that
utilizes global and local feature descriptors to extract features from the underlying data. Previously, ML tools have
been used to detect unusual texture in chest radiographs and to show extraction of texture and shape features and
classification with a binary classifier in the process of TB screening from CXRs. Algorithms based on morphology
have been made to extract features including circularity, size, contrast and local curvature of the lung nodules for the
classification of abnormal and normal CXRs.
In this study [5] author assess the feasibility of Deep Learning- based detection and classification of pathological
patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients. Patients with
previously diagnosed TB were enrolled in this prospective for observational study. A consumer-grade digital still
camera was used to take photographs of their CXRs. The images were stratified by pathological patterns into
classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal inspection. Image analysis was
done with commercially available Deep Learning software in two steps. Pathological areas were initially localized;
detected areas were then classified. Detection was evaluated using receiver operating characteristics (ROC) analysis,
and a confusion matrix was used for classification.

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
691
In another paper [6] a residual learning system is provided to simplify the training of networks that are considerably
deeper than those which were used in the past. The layers are clearly reformulated as learning residual functions
with reference to the layer inputs, instead of learning unreferenced functions. Inclusive factual evidence is provided
to show that these residual networks are easier to optimize, and can attain accuracy from significantly increased
detail. Dataset assesses residual nets with a depth of up to 152 layers on the ImageNet—8x deeper than VGG nets
yet still having lower complication. On the ImageNet dataset 3.57% error is attained by an ensemble of these
residual nets. On the ILSVRC classification task of 2015, this result won the 1st place. Analysis is also provided on
CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual
recognition tasks. Just because of extremely deep representations, 28% relative improvements on the COCO object
detection data set. The deep residual nets are foundations of submissions to ILSVRC& COCO 2015 competitions,
where it also won the 1st places on the tasks of ImageNet localization, ImageNet detection, COCO segmentation,
and COCO detection.
Hardware Requirement: -
Training of CNN is very expensive and it requires a lot of resources. From low level perspective it translates into
many multiplications of matrices. Modern Central Processing Units (CPUs) are not optimized for such computations
and therefore are not very good at it. This experiment was performed in Google Colaboratory - Colaboratory is a
free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory easily
write and execute code, save and share analyses, and access powerful computing resources, all for free from internet
browser. Colaboratory supports Python 2.7 and Python 3.6. Code is executed in a virtual machine dedicated to
Google’s account. Python3 Google Compute Engine (GPU), 358.72 GB of Hard drive, 12.72 GB of RAM.
Software Requirement: -
The model is created using the python programming language and with the KERAS deep learning framework.
Proposed Convolutional Neural Network: -
Convolutional Neural Network (CNN) is a particular implementation of a neural network used in machine learning
that specifically processes array data such as images, commonly used in machine learning applications applied at
medical images [7]. CNN uses weight sharing network structure and has the ability to minimize the number of
weights and complexity of the neural network [8]. It has shown an important ability to extract the mid-level and high
level abstractions gained from raw data [9]. As CNN is multi-layered and fully trainable hence it can capture highly
nonlinear mapping between inputs and outputs, [10]. CNN consists of input, output and multiple hidden layers.
These hidden layers consist of convolutional layers, pooling layers and fully connected layers [11]. As shown in
figurer 1. Convolutional Neural Networks (CNN) Layers it establishes a feed-forward group of deep networks,
where neuron receives an input (are images) for passing it through a series of hidden layers. Each hidden layer is
totally linked to all neurons of the preceding layer and where neurons in a single layer work independently without
sharing any connection. The final layer is called the output layer which represents the class scores in classification
settings. The hidden layers to build CNN architectures are pooling layers, convolutional layers, normalization layers
and fully connected layers [12]. Every Layer accepts an input 2D volume for converting it to an output 2D volume
through a differentiable function.
In Convolutional neural networks the information (images) is processed by layers of mathematical processing to
make sense. Convolutional neural network has few to millions of artificial neurons—known as units—set in a series
of layers. After preprocessing steps, the input layer receives images from the outside. This is the original data
(images) in the proposed model that the network aims to learn or process about. From the input layer, the data
(images) travels through one or more hidden units. The hidden unit’s task is to convert the input into something the
output unit can utilize, as shown in Figure 1.
Many of the neural networks are fully linked from one layer to another. These connections are weighted; the higher
the number, the greater influence one unit has on another, just like a human brain. As the data travels through every
unit the network is learning more about the data. The output unit is on the other side of the network, and this is
where the network responds to the data that it was given and processed.

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
692
Figure 1:-Architecture of CNN used for Tuberculosis Diagnostics
Preprocessing: -
The two main preprocessing steps are: (1) Padding (2) Resizing. These steps should be followed before input the
images to the model for classification as shown in Figure 2.
(1) Padding: Radiograph images (X-rays images) in the dataset are of variable size and most of the images have
only single color channel and some having three color channels. It is not possible to process images of variable
sizes in the model. So first all the images are converted to uniform dimensions applying some extra padding in
the image and make uniform dimension i.e., 4892 x 4892 pixel with Portable Network Graphics (PNG) format.
(2) Resize Image: Memory is the biggest challenges in convolutional neural networks (CNNs) today. The reason
for resize the images are to overcome the memory errors. So resize the images is one of the technique used to
solve this problem. CXR images after the padding stage is now resize to size 128 x 128 pixels in size. The
images were pre-processed before use to the network.

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
693
Figure 2:-Preprocessing
Dataset:-
In this paper, two publically available datasets are used. The dataset include is the Montgomery dataset [13] and
China Set - The Shenzhen set - Chest X-ray Database [14]. The details of the dataset are summarized in the Table 1.
Table 1:-Dataset Description
S.
No.
International Dataset
Description / Summary
Format
No. of X-rays
1
Montgomery County X-
ray Set
X-ray images in this data set have been
acquired from the tuberculosis control
program of the Department of Health and
Human Services of Montgomery County,
MD, USA.
PNG
138
2
China Set - The
Shenzhen set - Chest X-
ray Database
The standard digital image database for
Tuberculosis is created by the National
Library of Medicine, Maryland, USA in
collaboration with Shenzhen No.3 People’s
Hospital, Guangdong Medical College,
Shenzhen, China.
PNG
662
The Montgomery County X-ray dataset was created by U.S. National Library of Medicine (USNLM) using the
services of the health department at Montgomery County (MC), USA. It consists of 138 CXRs (58 CXRs have TB
and 80 have normal) as shown in figure 5.2 postero-anterior (PA) collected under MC’s tuberculosis screening
program. The sizes of all of the images are 4020X4892 or 4892 x 4020 pixels. China Set - The Shenzhen set - Chest
X-ray Database This dataset was created by USNLM in association with Guangdong Medical College, Shenzhen,
China. It consists of 662 CXRs; containing 336 TB manifested CXRs as shown in figure 5.3. The sizes of most of
the images are 3000 x 3000 pixels.
Experimental Results: -
Training and validation process is also divided into different parts, all data/images are randomly chosen for training
and all the data/images for cross validation are randomly chosen form the dataset. The accuracy and loss are
illustrated in different graphs.

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
694
(a) Results of proposed CNN model on Montgomery Dataset images:
In first part, the images’ total number for training and testing are 105 and 25 respectively; total numbers of epochs
are 100. The graph in Figure 3(a) indicates that the network has tried to memorize the training data and thus, is able
to get better accuracy on it. In the graph given below, the training accuracy is 100%. Similarly the validation
accuracy is also near 86% after the end of the 100 epochs. Also an interesting trend shows that validation accuracy
keeps uniform after around 15 epochs. Validation accuracy is near 83% while the validation loss is around 8% at the
end of the 100 epochs, also the trend where the training loss is near to 0% after 22 epochs, while the validation loss
keeps decreasing as shown in Figure 3(b).
Figure 3:-(a) First test - Training vs. Validation Accuracy (Montgomery Dataset image (b) First test - Training vs.
Validation Loss(Montgomery Dataset images)
In the second part, total number for training and testing are 82 and 56 respectively; total numbers of epochs are now
200 and numbers of hidden layers are 3. The graph in Figure 4(a) indicates that the network has tried to memorize
the training data and thus, is able to get better accuracy on it after 50 epochs. In the graph figure 5.11, the training
accuracy is 100% after 50 epochs. Similarly the validation accuracy is near 92% after the end of the 200 epochs. But
the validation accuracy not stagnant and keep fluctuating as shown in Figure 4(a). Validation accuracy is near 92%
at the end of 200 epochs, while the validation loss is around 0% at the end of the 200 epochs, also the trend where
the training loss is near to 0% after 50 epochs, while the validation loss keeps decreasing and increasing and not fix
at a particular value but at the end of 200 epochs validation loss is 8% as shown in Figure 4(b).
Figure 4:-(a) Second test - Training vs. Validation Accuracy(Montgomery Dataset images)(b) Second test -
Training vs. Validation Loss (Montgomery Dataset images)
(b) Results of proposed CNN model on China Set - The Shenzhen set
Training and validation process is also divided into different parts, all data/images are randomly chosen for training
and for cross validation from dataset. In the first test, some changes are made to improve more results the images’
total number for training and testing are 345 and 230 respectively; total numbers of epochs are 200 and the batch
size is now 50. The graph in Figure 5(a) indicates that the network has tried to memorize the training data and thus,
is able to get accuracy 100% after 175 epochs. Similarly the validation accuracy is near 79.57% after the end of the

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
695
200 epochs. Validation accuracy is near 79.57% at the end of 200 epochs, while the validation loss is 1.4030 at the
end of the 200 epochs, also the trend where the training loss is near to 0% after 200 epochs, while the validation loss
keeps fluctuating as shown in Figure 5(b).
Figure 5:-(a) First test - Training vs. Validation Accuracy(The Shenzhen set) (b) First test - Training vs. Validation
Loss(The Shenzhen set)
In second test, the images’ total number for training and testing are 460 and 115 respectively; total numbers of
epochs are 500 and this time batch size is now 100. The graph in Figure 6(a) indicates that the network has tried to
memorize the training data and thus, is able to get accuracy 100% after 100 epochs. Similarly the validation
accuracy is near 80.87% after the end of the 500 epochs. This shows better results when batch size increased as
shown in figure 6(a). The validation loss is 1.6596 at the end of the 500 epochs, also the trend where the training
loss is near to 0% at the end of 500 epochs.
Figure 6:-(a) Second test - Training vs. Validation Loss(The Shenzhen set) (b) Second test - Training vs. Validation
Loss(The Shenzhen set)
Conclusion:-
In this paper, a model for medical application of chest pathology detection in chest radiography using Convolutional
Neural Networks (CNN) and the propose of the model to address effective diagnosis of tuberculosis diseases on
chest radiography by doing the recognition and classification of pathological structures from classified anatomies
which will help doctors fasten the detection process for multiple diseases. Hence, providing them additional valuable
time to focus more on the curing the diseases. This model consists of a classification branch. Classification branch
performs as a uniform feature extraction classification network. The result of this model indicate that this model out
performs other methods, which use no extra training data and less preprocessing. Despite the fact that this model is
not prepared for clinical selection.
Future Work: -
In this research work, successfully recognized and classified tuberculosis disease using chest X-rays dataset
collected from different sources. Which is basically data collected from USA and China. In near future, I will collect
X-ray images from local hospitals to train and test the system to predict better results. I come to know that data

ISSN: 2320-5407 Int. J. Adv. Res. 7(4), 689-696
696
augmentation will also help to generate better results. I also have planned to work with more complex medical data
like Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) images.
Reference:-
1. https://www.who.int/gho/tb/tb_text/en/
2. https://chfs.ky.gov/agencies/dph/dehp/idb/Documents/2018tbfallnewsletter.pdf
3. Diagnostic Imaging Dataset. Diagnostic Imaging Dataset Annual Statistical. Technical report, 2017.
4. R. Sivaramakrishnan, Sameer Antani, Sema Candemir, Zhiyun Xue, Joseph Abuya, Marc Kohli, Philip
Alderson, George Thoma. (2018): Comparing deep learning models for population screening using chest
radiography.
5. A. S. Becker, C. Blu¨ thgen, V. D. Phi van, C. Sekaggya-Wiltshire, B. Castelnuovo, A. Kambugu, J. Fehr, T.
Frauenfelder. (2018): Detection of tuberculosis patterns in digital photographs of chest X-ray images using
Deep Learning. 328-335.
6. He, K., Zhang, X., Ren, S. and Sun, J. (2015): Deep Residual Learning for Image Recognition. 770–778.
7. Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., and Kim, N. (2017) : Deep learning in medical
imaging: general overview. Korean journal of radiology., 18: 570–584.
8. Mader, K. (2017): Train simple xray cnn, kaggle.
9. Mooney, P. (2017): Predicting pathologies in x-ray images, kaggle.
10. Kang, L., Kumar, J., Ye, P., Li, Y., and Doermann, D. (2014): Convolutional neural networks for document
image classification. In Pattern Recognition (ICPR), 22nd International Conference: 3168–3172.
11. Sudharshan, D. P. and Raj, S. (2018). Object recognition in images using convolutional neural network. 2nd
International Conference on Inventive Systems and Control (ICISC): 718–722.
12. Karpathy, A. (2016). Cs231n convolutional neural networks for visual recognition. Neural networks.
13. https://openi.nlm.nih.gov/imgs/collections/NLM-MontgomeryCXRSet.zip
14. https://ceb.nlm.nih.gov/repositories/tuberculosis-chest-x-ray-image-data-sets/.