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Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Joe Louis Paul Ignatius1,*, Sasirekha Selvakumar1, Kavin Gabriel Joe Louis Paul2, Aadhithya B. Kailash1, S. Keertivaas1, S. A. J. Akarvin Raja Prajan1

1 Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai (OMR), Kalavakkam, Chennai, 603110, Tamil Nadu, India
2 Gullas College of Medicine-University of Visayas, Banilad, Mandaue City, 6014, Cebu, Philippines

* Corresponding Author: Joe Louis Paul Ignatius. Email: email

Computer Systems Science and Engineering 2023, 44(1), 81-97. https://doi.org/10.32604/csse.2023.025195

Abstract

Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people’s lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmentation of interest, and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection. Furthermore, this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs. It was able to accurately detect TB from CXR images using pre-processing, data augmentation, and deep CNN models. Without histogram matching the best accuracy, sensitivity, specificity, precision and F1-score in the detection of TB using CXR images among ten models are 99.25%, 99.48%, 99.52%, 99.48% and 99.22% respectively. With histogram matching the best accuracy, sensitivity, specificity, precision and F1-score are 99.58%, 99.82%, 99.67%, 99.65% and 99.56% respectively. The proposed methodology, which has cutting-edge performance, will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

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Cite This Article

APA Style
Ignatius, J.L.P., Selvakumar, S., Paul, K.G.J.L., Kailash, A.B., Keertivaas, S. et al. (2023). Histogram matched chest x-rays based tuberculosis detection using CNN. Computer Systems Science and Engineering, 44(1), 81-97. https://doi.org/10.32604/csse.2023.025195
Vancouver Style
Ignatius JLP, Selvakumar S, Paul KGJL, Kailash AB, Keertivaas S, Prajan SAJAR. Histogram matched chest x-rays based tuberculosis detection using CNN. Comput Syst Sci Eng. 2023;44(1):81-97 https://doi.org/10.32604/csse.2023.025195
IEEE Style
J.L.P. Ignatius, S. Selvakumar, K.G.J.L. Paul, A.B. Kailash, S. Keertivaas, and S.A.J.A.R. Prajan "Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN," Comput. Syst. Sci. Eng., vol. 44, no. 1, pp. 81-97. 2023. https://doi.org/10.32604/csse.2023.025195



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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