=Paper=
{{Paper
|id=Vol-3058/paper72
|storemode=property
|title=A Deep Learning Approach For Tuberculosis Diagnosis From Chest X-Rays: A Survey
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-104.pdf
|volume=Vol-3058
|authors=Raghav Sharma,Preeti Gupta,Harvinder Kang
}}
==A Deep Learning Approach For Tuberculosis Diagnosis From Chest X-Rays: A Survey==
https://ceur-ws.org/Vol-3058/Paper-104.pdf
A Deep Learning Approach for Tuberculosis Diagnosis from chest
X-Rays: A Survey
Raghav Sharma1, Preeti Gupta2 and Harvinder Kaur3
123
University Institute of Engineering and Technology, Panjab University, Chandigarh 160023, India
Abstract
Deep Learning has been on the rise for various applications that includes but not limited to
autonomous driving, industries and medical imaging. Medical Imaging is one of the best data
sources having ability to assist in diagnosis of diseases, rehabilitation as well as in treatment.
The power of deep learning is that it can automatically learn from data and classify the images
with good accuracy that has led to path-breaking applications. The clear view and a structured
approach of applying deep learning in the health domain in order to assist in diagnosis and
treatment of diseases in accurate and precise manner is the beneficial aspect of technology. The
various aspects to utilize deep learning for diagnosis of Tuberculosis (TB) are important in
building of appropriate Computer Aided Diagnosis (CAD) system. In this paper, a
comprehensive overview of deep learning based Convolutional Neural Network (CNN) models
implemented specifically on Tuberculosis (TB) diseases. The chest X-rays are the most
economical and commonly used imaging technique, is the dataset considered in review. The
transfer learning utilization for detection of disease is explored with classification performance
analysis. The future challenges with the application of deep learning models and the new
directions of research needed in this field to achieve the practical performance requirements,
are discussed.
Keywords
Deep Learning, tuberculosis, chest X-rays, transfer learning
1. Introduction
Deep learning is inspired by the human brain’s deep structure and its algorithms use computational
methods to learn features of data. Deep learning makes use of neural networks to extract important
feature representations directly from data. Deep learning approach based Convolutional Neural
Network (CNN) algorithms that can learn from data images automatically, is reckoned effective on
medical images [1]. Tuberculosis (TB) is among top infectious diseases as reported by W.H.O and is
the major cause of deaths worldwide. Detection at an early stage of this infectious disease is highly
needed. The Chest X-Rays are commonly utilized in detection of TB as it is economically viable but
its manifestations rely on texture and geometric features so the accurate diagnosis requires highly
experienced medical staff [2]. Therefore, there is a high need for an automated screening system to
assist medical staff in the decision making process. Artificial intelligence helps indevelopment of such
high impact automated screening systems. The automated TB screening can be achieved by CNN
models as they give higher accuracy as compared to other methods [3]. The CNNs are made up of
various layers, i.e., convolution, pooling and fully connected layers A CNN takes a picture and runs it
through the network layers, producing a final class. These network layers learn the features of the
image.
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07,
2021, NITTTR Chandigarh, India
EMAIL: raghav97.rs@gmail.com (A. 1); preeti.uiet@gmail.com (A. 2); harvinderkang29@gmail.com (A. 3)
ORCID: 0000-0003-3430-0426 (A. 1)
©2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Filters at various resolutions are implemented on image and convolution with previous image is done
and output is fed to the next layer [5]. The simple and complex features are learned efficiently by
layers. The max pooling layer functions to convolve the features of the last layer. The last layers are
fully connected and soft max layers. Feature extraction is a simple task with deep learning and learned
features helps to train a classifier [6]. The several approaches for training a deep learning network are
used: such as training from scratch, transfer learning and semantic segmentation [8]. The transfer
learning approach is mainly utilized. The training parameters tuning is also important to get the better
results [9]. Data augmentation helps in improving the performance with smaller datasets. Dropout
regularization is highly impactful in improving the performance of CNNs. Batch normalization makes
the training of the network more efficient. CNNs consist of various parameters which are adjusted
throughout the training phase, with the help of large amount of calculations. The Graphical Processing
Unit(GPU) is suitable for parallel processing and helps to speed up calculations during the training
phase. MATLAB and Python programming languages are generally used to implement deep learning.
2. Deep Learning: An Overview
There are various approaches to train the deep learning network are training from scratch, transfer
learning and semantic segmentation to pre-train the network [10]. The training composed of accessing
data and preprocessing data next is configuring network layers, training the network and tuning the
parameters to achieve good performance of the system. The workflow is shown in figure 1.
Figure 1: the Deep Learning Workflow
2.1. Convolutional Neural Network
CNN is a feed-forward network and consists of various layers which are given below.
● Convolution layer: This layers outputs a feature map by convolving the input image with a
convolution filter. The convolution is controlled by stride. The operation learns the features
which are further utilized in classification.
● Pooling layer: This layer performs the pooling operations like average, sum to the spatial
dimension of the input. According to its function it is also known as a down sampling layer.
The Pooling and convolution layers are used in tandem in the development of network
architecture.
● Fully-connected layer: In this every neuron of the current layer is connected to each neuron in
the previous layer. The number of classes is determined by the total number of fully
connected neurons in the final layer. All neurons are connected, having a specific weight assigned
to each connection. This layer establishes a weighted sum of all the outputs from the previous
layer to determine aspecific target output.
● Rectified Linear Unit (ReLU): ReLU is an activation function implemented next to the
convolution layer. This layer entails mapping the output to the input set and projecting the
nonlinearity onto the network. [11].
● Batch normalization: It is done amidst the layers of network to create normalized activation
maps for each training batch. It maintains the regularization of the network and increases
training efficiency.
Figure 2: the CNN Architecture
2.1.1. Transfer Learning
Transfer learning is the process in which a pre-trained network's layers are transferred to other
networks for fine-tuning and feature extraction. The CNN training is done with large dataset initially
and after that small dataset is utilized to train the pre-trained model with fine tuning for efficient
performance of CNN model [12]. Transfer learning gives better results than training from scratch. The
performance further can be enhanced with optimization of hyper-parameters.
2.2. Classifiers
● Support vector machine (SVM): The SVM process is based on the separation of two classes by
determining a hyper plane which maximizes the margin size by optimization and minimizes the
miss-classification errors simultaneously [13].
● Soft max classifier: Softmax utilizes logistic regression to deal with multiple class outputs.
Each class in the problem is given decimal probabilities[4].
2.3. Training of the Convolutional Neural Network
Before training the training options are selected like learning rate, max epoch, mini-batch, depth,
layers, regularization etc. During training the CNN learns the features directly from the image. The
layers weights are learned during the training. The network is run and training is monitored. In case of
inappropriate results the parameters tuning and Bayesian optimization [21] can be implemented. The
learning rate is the most important hyper-parameter which controls the speed of the training. Several
optimizers such as RMS prop, Stochastic Gradient Descent(SGD) and Adaptive Moment Estimation
(Adam)[6] are there which work to adjust parameters within CNNs. The learning process consists of
feeding input data to CNNs, updation of parameters within CNNs and iteration with units of mini-
batch. When whole of the training data is used once it is called one epoch. Data is generally shuffled
and allocated to different groups for each epoch when using mini-batch learning.
2.4. Performance Metrics
CNN models can be evaluated efficiently and directly with statistical methods. Classification
accuracy of the model is calculated. The confusion matrix can be utilized to know the performance of
the created model. On the test datasets, Receiver-Operating-Characteristics(ROC) curve [6, 27] and
Area-under-Curve(AUC) [1] are determined. Contingency tables, sensitivity and specificity [1]
measured for performance analysis of the model. The recall, F1-score which combine precision and
recall [23] w.r.t positive class metrics measures of the baseline model determined for comparison of
different architectures.
3. Related Work
In [1] researcher proposed the CNN-based TB screening system utilizing pre-trained CNN AlexNet.
One extra convolution layer added to deal with high resolution medical images. The resized images
used in training to increase the performance efficiency. The parameter learning rate is tuned in decayed
mode with stochastic gradient optimization. The entire training process, from feature extraction to
classification is presented. The AUCs for three real-world datasets for the automatic TB screening
CAD system based on deep CNN were calculated. The initial layers learn low level features and high
level features were learned by higher layers during training. Data augmentation and transfer learning
is utilized to improve the performance measures. The performances on KIT, Montgomery and
Shenzhen datasets are compared. In [2] author demonstrated the three different proposals for TB
detection utilizing pre-trained networks such as Resnet and Googlenet as features extractors and then
support vector machine(SVM) classifier is trained with the extracted features of images. In the first
proposal resized images and in the second sub-regions of image are considered for extracting features.
The last model ensembles the trained classifiers. The findings show that ResNet gives stable
accuracies as compared to other CNNs. In [3] the author proposed the Deep CNN (DCNN) approach
based on automated TB disease classification. In this, the ensemble model of AlexNet and GoogleNet
is utilized to increase the performance measures. The both post processing and preprocessing data
augmentation techniques such as random cropping, mirror images, mean subtraction, rotations are
applied to the dataset. The Stochastic Gradient Descent Optimizer is considered during tuning of the
training parameters. Ensemble approach is utilized which is based on the implementation of different
weighted averages of the probability scores provided by the classifiers of both the CNNs. In [4] the
author presented the automatic TB detection system with region of interest features learning approach
and CNN VGG-Net is considered and accuracy about 80% was achieved. In[5] researcher presented the
method of classification of TB disease using CNN AlexNet and GoogleNet. The focus is mainly on the
optimization techniques for improve the performance of the network. The decay learning rate with
iterations and weights of dropout and ReLU layers are implemented to avoid over-fitting during
training. The improvement in accuracy from 53.02 to 85.68% was achieved using shuffle sampling
technique. In [6] the author developed a generalized model for medical image classification based on
decision tree approach. The accuracy of 81.25% and AUC of 0.99 with VGG- Net was achieved
utilizing the data augmentation, Adam optimizer, sigmoid activation function and SVM classifiers. In
[7] demonstrated the transfer learning on medical imaging for TB detection using improvement in
training weights. The impact of varying weights on learning is evaluated by performance metrics and
AUC 0.99 with ResNet-50 is achieved on Shenzhen dataset. In [8] the author applied data
augmentation on region of interest on image and HAAR and LBP features are utilized on ResNet
CNN. In [9] the author emphasized on modality specific learning for generalization utilizing large X-
rays dataset. The various CNN such as VGG-16, InceptionV3, ResNet and DenseNet-121 for TB
detection is used. The evaluation reveals that DenseNet-121 gives the best result with AUC 0.92 and
accuracy 0.897. In [11] the significance of using ReLU activation for the purpose of object
recognition as compared to the binary units and the several aspects of deep learning and recent clinical
studies are demonstrated. In [12] authors emphasized that better generalization performance can be
achieved on initializing with transfer weights after considerable fine tuning on a new task. In [13] author
demonstrated pre-trained deep CNN model and support vector machine for feature extraction and
classification for good results. In [14] author demonstrated the several aspects of deep learning and
recent clinical studies. In [15] author presented the performance of the model improved with larger
dataset and fine tuning of hyper-parameters. In [16] demonstrated pre-trained deep CNN model for
extraction of feature of input gives good results. In [17] the feasibility of CNN utilizing larger dataset
with learning set of low-level features including Scale Invariant Feature Transform (SIFT), GIST,
PHOG and SSIM. In [18] the ResNet CNN model by transfer learning the pre-training weights
extracted from the ImageNet to detect tuberculosis manifestations. In [19] author utilized textural
features as descriptors to categorize into TB or non-TB cases and able to detect this disease with
statistical features in the image histogram. In [20] author presented a statistical interpretation method
for detecting tuberculosis. In [21] author demonstrated the Bayesian classification for detection of TB.
In [22] author provided a textural and geometric feature-based classification of tuberculosis
manifestations. In [23] author proposed the two- stage classification method and achieved boost up in
the recall value. To perform identification and detection different sub-models are implemented. The
performance analysis demonstrated that resizing the dataset, normalization of data leads to faster
convergence. In [26] author demonstrated the various deep learning based CAD tools for detection of
TB diseases available commercially such as T- x net, CAD4TB etc. Considering the above literature
review, the several techniques utilized and CNN algorithm used for TB disease detection with the
performance achieved are summarized. Table 1 below shows the datasets used by authors in research
papers of review and Table 2 shows the summary of works carried out for TB Detection using deep
learning with CXRs.
Table 1
Different Datasets for Tuberculosis
Sr.no. Dataset Image size Number of Images Type of Image
1. Montgomery 4020*4892 138 Frontal
[24]
2. Shenzhen NA 662 Frontal
[24]
3. KIT [1] NA 10,848 NA
4. JSRT [25] 2048*2048 247 NA
Table 2
Brief of research carried out for Tuberculosis Detection utilizing Deep Learning with Chest X-rays
Imaging.
Sr. Author’s name Year Dataset Algorithm Processing Technique Accuracy
1. Hwang et al.[1] 2016 ● KIT ● AlexNet ● data augmentation
● Montgomery transfer learning from
● Shenzhen lower convolutional layer 82.6%
● decaying learning rate 83.4%
2. Lopes et al.[2] 2017 ● Montgomery ● GoogleNet ● CNNs as feature extractors 82.8%
● Shenzhen ● ResNet ● Support vector machine as classifier
● VGG-Net ● Image processing on datasets
3. Lakhani et al[3] 2017 ● Montgomery ● AlexNet ● DCNN approach 85.68%
● Shenzhen ● GoogleNet ● Ensemble model of AlexNet and
GoogleNet implemented on average of
weights of classifier
4. Hooda et al[4] 2018 ● JSRT ● AlexNet ● Softmax classification 82.09%
● Montgomery ● GoogleNet ● Modified CNN
● VGG-Net
5. Liu et al.[5] 2017 ● Shenzhen ● AlexNet ● Decaying learning rate and weights 85.68%
● GoogleNet ● shuffle sampling technique for data
augmentation
6. Ahsan et al.[6] 2017 ● Shenzhen ● VGG-Net ● CNN reapplied on augmented images 81.25%
7. Nyugen et.al.[7] 2019 ● Shenzhen ● Inception ● Varying weights technique
● Montgomery ● ResNetV2
● NIH-14 ● Dense Net
8. R.S. Gorakhvi[8] 2019 ● Shenzhen ● ResNet-18 ● Data augmentation with the help of 81.33%
● Montgomery HAAR and LBP features and cropped
ROI.
● Data generated with augmentation
used along with the original dataset
for training
9. Alcantara et al.[9] 2017 ● BMC ● GoogleNet ● Supervised training using larger 89.6%
● BUMC dataset
● Fine tuning the CNN for smaller
datasets
● ROI based feature extraction
● SVM Classification
10. Liu et al[10] 2019 ● Pulmonary Chest ● Self- designed ● Hyper parameter optimization 87%
X-ray CNN ● Masked algorithm used on the dataset
abnormalities to remove noise
4. Conclusion
This paper reviews deep learning methods used in several papers on TB screening, based on chest
X-Ray Imaging. The performances of several CNN algorithms are analyzed and observed that
performance of CNNs are highly reliable for detection and classification of TB disease. It is observed
that deep learning success depends on labelled public datasets. It is also seen that the speed and accuracy
of the network improves by data augmentation and optimization. The fine practical settings also aid in
improvement of the model performance. A comprehensive review helps the researchers in choice of
the suitable algorithm and to setup the appropriate framework for their research. Although these
researches provide good accuracy, still the new directions in standardized framework specifically for
medical images and computer vision is needed. The study of literature describes the deep learning
methods surpassing the state of art in medical image domains. Finally, on the basis of tuning
parameters we suggest that optimization usage is most helpful in improving the performance easily. In
future, the deep learning based diagnosis tool available worldwide for TB disease will leads to more
efficient, very low cost and fast results having the techniques within the deep learning software for
image visualizations.
.
5. References
[1] S. S. Hwang, H.-E. Kim, J. Jeong, and H.-J. Kim, “A novel approach for TB screening based on
deep convolutional neural networks,” 2016, doi: 10.1117/12.2216198.
[2] U. K. Lopes and J. F. Valiati, “Pre-trained convolutional neural networks as feature extractors
for TB detection,” Computers in Biology and Medicine, 2017, doi:
10.1016/j.compbiomed.2017.08.001.
[3] P. Lakhani and B. Sundaram, “Deep learning at chest radiography: Automated classification of
pulmonary TB by using convolutional neural networks,” Radiology, 2017, doi:
10.1148/radiol.2017162326.
[4] R. Hooda, A. Mittal, and S. Sofat, “An Efficient Variant of Fully-Convolutional Network for
Segmenting Lung Fields from Chest Radiographs,” Wireless Personal Communications, 2018, doi:
10.1007/s11277-018-5777-3.
[5] Y. Cao et al., “Improving TB Diagnostics Using Deep Learning and Mobile Health Technologies
among Resource-Poor and Marginalized Communities,” 2016, doi: 10.1109/CHASE.2016.18.
[6] M. Ahsan, R. Gomes, and A. Denton, “Application of a convolutional neural network using
transfer learning for TB detection,” 2019, doi: 10.1109/EIT.2019.8833768.
[7] Q. H. Nguyen et al., “Deep Learning Models for TB Detection from Chest X-ray Images,” 2019,
doi: 10.1109/ICT.2019.8798798.
[8] R. S. Ghorakavi, “TBNet:Pulmonary TB Diagnosing System using Deep Neural Networks,”
ArXiv190208897 Cs, Feb. 2019, Accessed: Jul. 15, 2020. [Online]. Available:
http://arxiv.org/abs/1902.08897.
[9] M. F. Alcantara et al., “Improving TB diagnostics using deep learning and mobile health
technologies among resource-poor communities in Perú,” Smart Health, 2017, doi:
10.1016/j.smhl.2017.04.003.
[10] X. Liu, H. Lei, and S. Han, “TB Detection from Computed Tomography with Convolutional
Neural Networks,” Advances in Computed Tomography, vol. 08, no. 04, pp. 47–56, 2019, doi:
10.4236/act.2019.84005.
[11] V. Nair and G. E. Hinton, “Rectified linear units improve Restricted Boltzmann machines,” in
ICML 2010 - Proceedings, 27th International Conference on Machine Learning, 2010, pp. 807–814.
[12] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural
networks?,” in Advances in Neural Information Processing Systems, 2014, vol. 4, no. January, pp.
3320–3328.
[13] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other
Kernel-based Learning Methods. Cambridge University Press, 2000.
[14] K. Yasaka, H. Akai, A. Kunimatsu, S. Kiryu, and O. Abe, “Deep learning with convolutional
neural network in radiology,” Japanese Journal of Radiology. 2018, doi: 10.1007/s11604-018-0726-3.
[15] R. Shen, I. Cheng, and A. Basu, “A hybrid knowledge-guided detection technique for screening
of infectious pulmonary TB from chest radiographs,” IEEE Transactions on Biomedical Engineering,
2010, doi: 10.1109/TBME.2010.2057509.
[16] C. Qin, D. Yao, Y. Shi, and Z. Song, “Computer-aided detection in chest radiography based on
artificial intelligence: A survey,” BioMedical Engineering Online. 2018, doi: 10.1186/s12938-018-
0544-y.
[17] R. N. Rohmah, A. Susanto, and I. Soesanti, “Lung TB identification based on statistical feature
of thoracic X-ray,” 2013, doi: 10.1109/QiR.2013.6632528.
[18] Z. Z. Qin et al., “Using artificial intelligence to read chest radiographs for TB detection: A multi-
site evaluation of the diagnostic accuracy of three deep learning systems,” Scientific Reports, 2019, doi:
10.1038/s41598-019-51503-3.
[19] Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan, “Chest pathology
detection using deep learning with non-medical training,” 2015, doi: 10.1109/ISBI.2015.7163871.
[20] J. Ma, Y. Song, X. Tian, Y. Hua, R. Zhang, and J. Wu, “Survey on deep learning for pulmonary
medical imaging,” Frontiers of Medicine. 2019, doi: 10.1007/s11684-019-0726-4.
[21] R. H. Abiyev and M. K. S. Ma’aitah, “Deep Convolutional Neural Networks for Chest Diseases
Detection,” Journal of Healthcare Engineering, 2018, doi: 10.1155/2018/4168538.
[22] D. S. W. Ting, P. H. Yi, and F. Hui, “Clinical applicability of deep learning system in detecting
TB with chest radiography,” Radiology. 2018, doi: 10.1148/radiol.2017172407.
[23] H. Abe, H. MacMahon, J. Shiraishi, Q. Li, R. Engelmann, and K. Doi, “Computer-aided
diagnosis in chest radiology,” Seminars in Ultrasound, CT and MRI, 2004, doi:
10.1053/j.sult.2004.02.004.
[24] S. Jaeger, S. Candemir, S. Antani, Y.-X. J. Wáng, P.-X. Lu, and G. Thoma, “Two public chest
X-ray datasets for computer-aided screening of pulmonary diseases.” Quantitative imaging in medicine
and surgery, 2014, doi: 10.3978/j.issn.2223-4292.2014.11.20.
[25] J. Shiraishi et al., “Development of a digital image database for chest radiographs with and
without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of
pulmonary nodules,” American Journal of Roentgenology, 2000, doi: 10.2214/ajr.174.1.1740071.
[26] Qin, Z. Z., Naheyan, T., Ruhwald, M., Denkinger, C. M., Gelaw, S., Nash, M. ... & Kik, S. V.
(2021). A new resource on artificial intelligence powered computer automated detection software
products for tuberculosis programmes and implementers. Tuberculosis, 127,102049.