{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:04:01Z","timestamp":1791914641648,"version":"4.3.4"},"reference-count":52,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Chest disease identification for Tuberculosis and Pneumonia diseases presents diagnostic challenges due to overlapping radiographic features and the limited availability of expert radiologists, especially in developing countries. The present study aims to address these challenges by developing a Computer-Aided Diagnosis (CAD) system to provide consistent and objective analyses of chest X-ray images, thereby reducing potential human error. By leveraging the complementary strengths of convolutional neural networks (CNNs) and vision transformers (ViTs), we propose a hybrid model for the accurate detection of Tuberculosis and for distinguishing between Tuberculosis and Pneumonia. Methods: We designed a two-step hybrid model that integrates the ResNet-50 CNN with the ViT-b16 architecture. It uses the transfer learning on datasets from Guangzhou Women\u2019s and Children\u2019s Medical Center for Pneumonia cases and datasets from Qatar and Dhaka (Bangladesh) universities for Tuberculosis cases. CNNs capture hierarchical structures in images, while ViTs, with their self-attention mechanisms, excel at identifying relationships between features. Combining these approaches enhances the model\u2019s performance on binary and multi-class classification tasks. Results: Our hybrid CNN-ViT model achieved a binary classification accuracy of 98.97% for Tuberculosis detection. For multi-class classification, distinguishing between Tuberculosis, viral Pneumonia, and bacterial Pneumonia, the model achieved an accuracy of 96.18%. These results underscore the model\u2019s potential in improving diagnostic accuracy and reliability for chest disease classification based on X-ray images. Conclusions: The proposed hybrid CNN-ViT model demonstrates substantial potential in advancing the accuracy and robustness of CAD systems for chest disease diagnosis. By integrating CNN and ViT architectures, our approach enhances the diagnostic precision, which may help to alleviate the burden on healthcare systems in resource-limited settings and improve patient outcomes in chest disease diagnosis.<\/jats:p>","DOI":"10.3390\/diagnostics14232754","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T09:55:20Z","timestamp":1733478920000},"page":"2754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Yousra","family":"Hadhoud","sequence":"first","affiliation":[{"name":"LAMIS Laboratory, Larbi Tebessi University, Tebessa 12002, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4791-4227","authenticated-orcid":false,"given":"Tahar","family":"Mekhaznia","sequence":"additional","affiliation":[{"name":"LAMIS Laboratory, Larbi Tebessi University, Tebessa 12002, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3185-8806","authenticated-orcid":false,"given":"Akram","family":"Bennour","sequence":"additional","affiliation":[{"name":"LAMIS Laboratory, Larbi Tebessi University, Tebessa 12002, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Amroune","sequence":"additional","affiliation":[{"name":"LAMIS Laboratory, Larbi Tebessi University, Tebessa 12002, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neesrin Ali","family":"Kurdi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz Hadi","family":"Aborujilah","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, College of Commerce & Business Administration, Dhofar University, Salalaha 211, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7172-8224","authenticated-orcid":false,"given":"Mohammed","family":"Al-Sarem","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Aylol University College, Yarim 547, Yemen"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1016\/j.chest.2022.01.014","article-title":"Global Impact of Respiratory Disease","volume":"161","author":"Levine","year":"2022","journal-title":"Chest"},{"key":"ref_2","unstructured":"(2024, May 28). World Health Organization. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/chronic-obstructive-pulmonary-disease-(copd)."},{"key":"ref_3","unstructured":"World Health Organization (2024, May 28). Tuberculosis. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/tuberculosis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"297","DOI":"10.15585\/mmwr.mm7212a1","article-title":"Tuberculosis\u2014United States, 2022","volume":"72","author":"Schildknecht","year":"2023","journal-title":"MMWR. Morb. Mortal. Wkly. Rep."},{"key":"ref_5","unstructured":"World Health Organization (2024, October 11). Global Tuberculosis Report 2024. Available online: https:\/\/www.who.int\/teams\/global-tuberculosis-programme\/tb-reports\/global-tuberculosis-report-2024."},{"key":"ref_6","unstructured":"UNICEF (2024, May 28). Pneumonia in Children: What You Need to Know. Available online: https:\/\/www.unicef.org\/stories\/childhood-pneumonia-explained#:~:text=Every year%2C pneumonia claims the,of these deaths are preventable."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Qin, C., Yao, D., Shi, Y., and Song, Z. (2018). Computer-aided detection in chest radiography based on artificial intelligence: A survey. Biomed. Eng. Online, 17.","DOI":"10.1186\/s12938-018-0544-y"},{"key":"ref_8","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_9","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the ICLR 2021\u20149th International Conference on Learning Representations, Virtual Event, Austria."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chernyavskiy, A., Ilvovsky, D., and Nakov, P. (2021). Transformers: \u201cThe End of History\u201d for Natural Language Processing?. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-030-86523-8_41"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118576","DOI":"10.1016\/j.eswa.2022.118576","article-title":"ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network [Formula presented]","volume":"211","author":"Nahiduzzaman","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4756","DOI":"10.1007\/s10489-024-05420-2","article-title":"A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system","volume":"54","author":"Sanida","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"483","DOI":"10.5114\/pjr.2023.132533","article-title":"An improvement of the CNN-XGboost model for pneumonia disease classification","volume":"88","author":"Hedhoud","year":"2023","journal-title":"Pol. J. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1016\/j.aej.2022.10.053","article-title":"A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images","volume":"64","author":"Alshmrani","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"31803","DOI":"10.1007\/s11042-021-11192-5","article-title":"Deep learning based detection of COVID-19 from chest X-ray images","volume":"80","author":"Guefrechi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1007\/s12559-020-09787-5","article-title":"Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19","volume":"16","author":"Ibrahim","year":"2024","journal-title":"Cognit. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"39243","DOI":"10.1109\/ACCESS.2023.3267492","article-title":"A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-ray Images","volume":"11","author":"Malik","year":"2023","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hu, X., Chen, J., and Chen, Y. (2024). RegMamba: An Improved Mamba for Medical Image Registration. Electronics, 13.","DOI":"10.3390\/electronics13163305"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1142\/S2196888822500348","article-title":"Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks","volume":"10","author":"Pham","year":"2023","journal-title":"Vietnam J. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1038\/s41597-022-01498-w","article-title":"VinDr-CXR: An open dataset of chest X-rays with radiologist\u2019s annotations","volume":"9","author":"Nguyen","year":"2022","journal-title":"Sci. Data"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1016\/j.acra.2022.11.027","article-title":"Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19","volume":"30","author":"Huang","year":"2023","journal-title":"Acad. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2625","DOI":"10.1016\/j.injury.2022.04.013","article-title":"Vision Transformer for femur fracture classification","volume":"53","author":"Tanzi","year":"2022","journal-title":"Injury"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/5863782","article-title":"Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism","volume":"2022","author":"Shou","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C., Jin, Y., Liang, J., Yang, Y.H., Nie, S., and Hai, Y. (2023, January 5\u20138). Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, T\u00fcrkiye.","DOI":"10.1109\/BIBM58861.2023.10385862"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"012175","DOI":"10.1088\/1742-6596\/2010\/1\/012175","article-title":"Automatic diagnosis of COVID-19 using a tailored transformer-like network","volume":"2010","author":"Liu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.59275\/j.melba.2020-48g7","article-title":"COVID-19 Image Data Collection: Prospective Predictions are the Future","volume":"1","author":"Cohen","year":"2020","journal-title":"Mach. Learn. Biomed. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI Help in Screening Viral and COVID-19 Pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chetoui, M., and Akhloufi, M.A. (2022). Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays. J. Clin. Med., 11.","DOI":"10.3390\/jcm11113013"},{"key":"ref_31","unstructured":"SIIM-FISABIO-RSNA (2024, June 20). SIIM-FISABIO-RSNA COVID-19 Detection Challenge. Available online: https:\/\/siim.org\/research-journal\/siim-machine-learning-challenges\/covid-19-kaggle-challenge\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Krishnan, K.S., and Krishnan, K.S. (2021, January 7\u20139). Vision Transformer based COVID-19 Detection using Chest X-rays. Proceedings of the IEEE International Conference on Signal Processing, Computing and Control, Solan, India.","DOI":"10.1109\/ISPCC53510.2021.9609375"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Than, J.C.M., Thon, P.L., Rijal, O.M., Kassim, R.M., Yunus, A., Noor, N.M., and Then, P. (2021, January 9\u201310). Preliminary Study on Patch Sizes in Vision Transformers (ViT) for COVID-19 and Diseased Lungs Classification. Proceedings of the 2021 IEEE National Biomedical Engineering Conference (NBEC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/NBEC53282.2021.9618751"},{"key":"ref_34","unstructured":"Taslimi, S., Taslimi, S., Fathi, N., Salehi, M., and Rohban, M.H. (2022). SwinCheX: Multi-label classification on chest X-ray images with transformers. arXiv."},{"key":"ref_35","unstructured":"National Institutes of Health Chest X-Ray\u2014Chris Crawford (2024, June 10). NIH Chest X-Rays Dataset. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/nih-chest-xrays\/data."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1007\/s10278-022-00666-z","article-title":"Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography","volume":"35","author":"Usman","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_37","first-page":"590","article-title":"CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison","volume":"33","author":"Irvin","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1007\/s42979-023-01818-w","article-title":"Deep Learning Methods for Chest Disease Detection Using Radiography Images","volume":"4","author":"Nasser","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102299","DOI":"10.1016\/j.media.2021.102299","article-title":"Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification","volume":"75","author":"Park","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.21037\/qims-23-1280","article-title":"Research and implementation of multi-disease diagnosis on chest X-ray based on vision transformer","volume":"14","author":"Huang","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s11517-022-02746-2","article-title":"PneuNet: Deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer","volume":"61","author":"Wang","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Okolo, G.I., Katsigiannis, S., and Ramzan, N. (2022). IEViT: An enhanced vision transformer architecture for chest X-ray image classification. Comput. Methods Programs Biomed., 226.","DOI":"10.1016\/j.cmpb.2022.107141"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.jare.2022.08.021","article-title":"A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images","volume":"48","author":"Ukwuoma","year":"2023","journal-title":"J. Adv. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"86710","DOI":"10.1109\/ACCESS.2022.3199419","article-title":"Convolution Neural Network With Coordinate Attention for the Automatic Detection of Pulmonary Tuberculosis Images on Chest X-Rays","volume":"10","author":"Xu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rajaraman, S., Zamzmi, G., Folio, L.R., and Antani, S. (2022). Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.864724"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"101797","DOI":"10.1016\/j.media.2020.101797","article-title":"PadChest: A large chest X-ray image dataset with multi-label annotated reports","volume":"66","author":"Bustos","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ejiyi, C.J., Qin, Z., Nnani, A.O., Deng, F., Ejiyi, T.U., Ejiyi, M.B., Agbesi, V.K., and Bamisile, O. (2024). ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images. Comput. Methods Programs Biomed. Updat., 5.","DOI":"10.1016\/j.cmpbup.2023.100133"},{"key":"ref_48","unstructured":"\u00d6zt\u00fcrk, \u015e., Tural\u0131, M.Y., and \u00c7ukur, T. (2023). HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 22\u201325). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","article-title":"Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_51","unstructured":"Chest X-Ray Images (Pneumonia) (2024, November 12). Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/paultimothymooney\/chest-xray-pneumonia\/data."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"115519","DOI":"10.1016\/j.eswa.2021.115519","article-title":"Detection of tuberculosis from chest X-ray images: Boosting the performance with vision transformer and transfer learning","volume":"184","author":"Duong","year":"2021","journal-title":"Expert Syst. Appl."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2754\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:48:57Z","timestamp":1760114937000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":52,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14232754"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14232754","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,6]]}}}