Abstract
Respiratory infections such as COVID-19, tuberculosis (TB) and pneumonia, remain important global health challenges, often requiring rapid and accurate diagnosis to prevent complications. Due to the visual similarities in chest X-ray (CXR) images, distinguishing between these diseases can be complex. In this study, we proposed, a deep learning (DL)-based model utilizing a customized VGG-19 architecture for multiclass classification of lung diseases, including COVID-19, pneumonia, TB, and healthy cases. A total of 5,928 CXR images were collected from open-access platforms, comprising COVID-19, pneumonia, TB, and normal cases. The dataset was pre-processed using bilateral filtering for noise suppression and Multiscale Retinex for image enhancement. FFurthermore, data augmentation and image resizing were also applied to increase robustness. When compared with the state-of-the-art techniques, the proposed method achieved a classification accuracy of 98.48% in identifying various lung disorders, with precision at 97% and an F1-score of 96%, indicating that it is an appropriate technique for computerized lung disease diagnosis in clinical environments.
References
Mwendo, Irad, Kinyua Gikunda, and Anthony Maina. "Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images--A Review." arXiv preprint arXiv:2303.16754 (2023).
Yadav, Sapna, Syed Afzal Murtaza Rizvi, and Pankaj Agarwal. "Detection of lung diseases for Pneumonia, tuberculosis, and covid-19 with artificial intelligence tools." SN Computer Science 5, no. 3 (2024): 303.
Ahmed, Mohammed Salih, Atta Rahman, Faris AlGhamdi, Saleh AlDakheel, Hammam Hakami, Ali AlJumah, Zuhair AlIbrahim, Mustafa Youldash, Mohammad Aftab Alam Khan, and Mohammed Imran Basheer Ahmed. "Joint diagnosis of Pneumonia, COVID-19, and tuberculosis from Chest X-Ray images: A deep learning approach." Diagnostics 13, no. 15 (2023): 2562.
Venkataramana, Lokeswari, D. Venkata Vara Prasad, S. Saraswathi, C. M. Mithumary, R. Karthikeyan, and N. Monika. "Classification of COVID-19 from tuberculosis and Pneumonia using deep learning techniques." Medical & Biological Engineering & Computing 60, no. 9 (2022): 2681-2691.
Wang, Wenkong, Weijie Huang, Quanli Lu, Jiyang Chen, Menghua Zhang, Jia Qiao, and Yong Zhang. "Attention mechanism-based deep learning method for hairline fracture detection in hand X-rays." Neural Computing and Applications 34, no. 21 (2022): 18773-18785.
Rajpurkar, Pranav, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding et al. "Chexnet: Radiologist-level Pneumonia detection on Chest X-Rays with deep learning." arXiv preprint arXiv:1711.05225 (2017).
Rahman, Tawsifur, Amith Khandakar, Muhammad Abdul Kadir, Khandaker Rejaul Islam, Khandakar F. Islam, Rashid Mazhar, Tahir Hamid et al. "Reliable tuberculosis detection using Chest X-Ray with deep learning, segmentation and visualization." Ieee Access 8 (2020): 191586-191601.
El Asnaoui, Khalid, and Youness Chawki. "Using X-ray images and deep learning for automated detection of coronavirus disease." Journal of Biomolecular Structure and Dynamics 39, no. 10 (2021): 3615-3626.
Narin, Ali, Ceren Kaya, and Ziynet Pamuk. "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks." Pattern Analysis and Applications 24, no. 3 (2021): 1207-1220.
Mandindi, Nkcubeko Umzubongile Siphamandla. "Detecting and Understanding COVID-19 Misclassifications: A Deep Learning and Explainable AI Approach." Master's thesis, University of the Witwatersrand, Johannesburg (South Africa), 2023.
Bharati, Subrato, Prajoy Podder, and M. Rubaiyat Hossain Mondal. "Hybrid deep learning for detecting lung diseases from X-ray images." Informatics in Medicine Unlocked 20 (2020): 100391.
Zavrak, Sultan. "Enhancing covid-19 diagnosis through vision transformer-based analysis of Chest X-Ray images." arXiv preprint arXiv:2306.06914 (2023).
Krishnan, Koushik Sivarama, and Karthik Sivarama Krishnan. "Vision transformer based COVID-19 detection using Chest X-Rays." In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE, 2021, 644-648.
Qi, Xiao, David J. Foran, John L. Nosher, and Ilker Hacihaliloglu. "Multi-feature vision transformer via self-supervised representation learning for improvement of covid-19 diagnosis." In Workshop on Medical Image Learning with Limited and Noisy Data, pp. 76-85. Cham: Springer Nature Switzerland, 2022.
Hadhoud, Yousra, Tahar Mekhaznia, Akram Bennour, Mohamed Amroune, Neesrin Ali Kurdi, Abdulaziz Hadi Aborujilah, and Mohammed Al-Sarem. "From binary to multi-class classification: A two-step hybrid cnn-vit model for chest disease classification based on x-ray images." Diagnostics 14, no. 23 (2024): 2754.
Yulvina, Rizka, Stefanus Andika Putra, Mia Rizkinia, Arierta Pujitresnani, Eric Daniel Tenda, Reyhan Eddy Yunus, Dean Handimulya Djumaryo, Prasandhya Astagiri Yusuf, and Vanya Valindria. "Hybrid Vision Transformer and Convolutional Neural Network for Multi-Class and Multi-Label Classification of Tuberculosis Anomalies on Chest X-Ray." Computers 13, no. 12 (2024): 343.
Slika, Bouthaina, Fadi Dornaika, Hamid Merdji, and Karim Hammoudi. "Lung Pneumonia severity scoring in Chest X-Ray images using transformers." Medical & Biological Engineering & Computing 62, no. 8 (2024): 2389-2407.
Kumar, Rahul, Cheng-Tang Pan, Yi-Min Lin, Shiue Yow-Ling, Ting-Sheng Chung, and Uyanahewa Gamage Shashini Janesha. "Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging." Diagnostics 15, no. 3 (2025): 248.
Kabir, Md Mohsin, M. F. Mridha, Ashifur Rahman, Md Abdul Hamid, and Muhammad Mostafa Monowar. "Detection of COVID-19, Pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation." Heliyon 10, no. 5 (2024).
Ou, Chih-Ying, I-Yen Chen, Hsuan-Ting Chang, Chuan-Yi Wei, Dian-Yu Li, Yen-Kai Chen, and Chuan-Yu Chang. "Deep learning-based classification and semantic segmentation of lung tuberculosis lesions in Chest X-Ray images." Diagnostics 14, no. 9 (2024): 952.
Sharma, Vinayak, Sachin Kumar Gupta, and Kaushal Kumar Shukla. "Deep learning models for tuberculosis detection and infected region visualization in Chest X-Ray images." Intelligent Medicine 4, no. 2 (2024): 104-113.
Oltu, Burcu, Selda Güney, Seniha Esen Yuksel, and Berna Dengiz. "Automated classification of Chest X-Rays: a deep learning approach with attention mechanisms." BMC Medical Imaging 25, no. 1 (2025): 71.
Kishore, Jaydeep, Akshita Jain, K. Krishna Koushika, Pawan Kumar Mishra, Shekhar Karanwal, and Surendra Solanki. "Enhancing medical diagnosis on Chest X-Rays: knowledge distillation from self-supervised based model to compressed student model." Discover Computing 28, no. 1 (2025): 118.
Chowdhury, Muhammad EH, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam et al. "Can AI help in screening viral and COVID-19 Pneumonia?." Ieee Access 8 (2020): 132665-132676.
Kumar, Chinthakindi Kiran, Gaurav Sethi, and Kirti Rawal. "Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications." International Journal of Electrical and Electronics Research 11, no. Special issue (2023): 01-09.