{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:56:29Z","timestamp":1781718989170,"version":"3.54.5"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    The diagnosis of lung diseases such as pneumonia and tuberculosis remains a major global health challenge, especially in resource-limited regions. Artificial Intelligence (AI) has shown strong potential in analyzing Chest X-Rays (CXR) for accurate and timely diagnosis, but most existing models are computationally heavy and lack interpretability, limiting their practical application. In this study, we present LXNet, a lightweight and explainable Convolutional Neural Network (CNN) for nine-class lung disease classification (\n                    <jats:italic>Normal<\/jats:italic>\n                    ,\n                    <jats:italic>Pneumonia<\/jats:italic>\n                    ,\n                    <jats:italic>Higher Density<\/jats:italic>\n                    ,\n                    <jats:italic>Lower Density<\/jats:italic>\n                    ,\n                    <jats:italic>Obstructive Pulmonary Diseases<\/jats:italic>\n                    ,\n                    <jats:italic>Degenerative Infectious Diseases<\/jats:italic>\n                    ,\n                    <jats:italic>Encapsulated Lesions<\/jats:italic>\n                    ,\n                    <jats:italic>Mediastinal Changes<\/jats:italic>\n                    and\n                    <jats:italic>Chest Changes<\/jats:italic>\n                    ). The model was evaluated on a diverse CXR dataset containing 6,743 images collected from a private imaging center (GRS Imagem, Brazil), enabling comprehensive multiclass assessment. LXNet contains only 0.35 million parameters and employs a no-pooling final block to preserve subtle diagnostic features while maintaining very low computational cost. Robustness was enhanced through adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE), grayscale normalization and stratified class balancing. LXNet was benchmarked against pretrained CNNs (DenseNet201, ResNet50V2 and InceptionV3) under identical settings. Explainable AI (Grad-CAM, Score-CAM and LIME) provided meaningful visualizations. LXNet achieved 96.1% accuracy in 5-fold cross validation, outperforming the baselines (DenseNet201: 90.3%, InceptionV3: 88.9%) by 1\u20138%, with only 308 seconds of training on standard hardware. Statistical significance was confirmed using Wilcoxon signed-rank tests (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.03125). These results demonstrate LXNet\u2019s promising performance and interpretability; however, reduced external performance indicates limited generalizability and its clinical applicability requires further validation.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0351762","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:35:35Z","timestamp":1781717735000},"page":"e0351762","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["LXNet: A lightweight CNN for lung disease classification from Chest X-ray with XAI-based interpretability"],"prefix":"10.1371","volume":"21","author":[{"given":"Juiria","family":"Humayan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Najmus Sakib","family":"Nahid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Sohel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7136-6339","authenticated-orcid":true,"given":"Md Alamgir","family":"Kabir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0713-0740","authenticated-orcid":true,"given":"Md Shakhawat","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6574-9969","authenticated-orcid":true,"given":"Zahid","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9149-2810","authenticated-orcid":true,"given":"Mona","family":"Jamjoom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"pone.0351762.ref001","unstructured":"World Health Organization. Tuberculosis. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/tuberculosis 2025 September 17."},{"key":"pone.0351762.ref002","unstructured":"BRAC. Bangladesh intensifies fight against Tuberculosis: Key stakeholders convene to mitigate challenges; 2024. Accessed: 2025-09-17. https:\/\/www.brac.net\/latest-news\/item\/1423-bangladesh-intensifies-fight-against-tuberculosis-key-stakeholders-convene-to-mitigate-challenges"},{"issue":"20","key":"pone.0351762.ref003","doi-asserted-by":"crossref","first-page":"13695","DOI":"10.3390\/ijerph192013695","article-title":"The Perception of Health Professionals in Bangladesh toward the Digitalization of the Health Sector","volume":"19","author":"MS Hossain","year":"2022","journal-title":"Int J Environ Res Public Health"},{"key":"pone.0351762.ref004","first-page":"67890","article-title":"A deep convolutional neural network model for lung disease classification based on x-ray images","volume":"15","author":"X Wang","year":"2025","journal-title":"Scientific Reports"},{"key":"pone.0351762.ref005","unstructured":"Hagen J. Survey: 60% of adults uncomfortable with AI in their healthcare. https:\/\/www.mobihealthnews.com\/news\/survey-60-adults-uncomfortable-ai-their-healthcare 2023. 2025 October 14."},{"key":"pone.0351762.ref006","doi-asserted-by":"crossref","first-page":"109370","DOI":"10.1016\/j.compeleceng.2024.109370","article-title":"A review of Explainable Artificial Intelligence in healthcare","volume":"118","author":"Z Sadeghi","year":"2024","journal-title":"Computers and Electrical Engineering"},{"issue":"1","key":"pone.0351762.ref007","doi-asserted-by":"crossref","first-page":"6650","DOI":"10.1038\/s41598-025-90607-x","article-title":"Explainable hybrid transformer for multi-classification of lung disease using chest X-rays","volume":"15","author":"X Fu","year":"2025","journal-title":"Sci Rep"},{"key":"pone.0351762.ref008","doi-asserted-by":"crossref","first-page":"1393758","DOI":"10.3389\/fdata.2024.1393758","article-title":"Data-driven classification and explainable-AI in the field of lung disease radiology: a comprehensive review","volume":"7","author":"STH Shah","year":"2024","journal-title":"Frontiers in Big Data"},{"key":"pone.0351762.ref009","doi-asserted-by":"crossref","first-page":"84954","DOI":"10.1109\/ACCESS.2025.3568463","article-title":"High-Performance Lung Disease Identification and Explanation Using a ReciproCAM-Enhanced Lightweight Convolutional Neural Network","volume":"13","author":"H-S Choi","year":"2025","journal-title":"IEEE Access"},{"key":"pone.0351762.ref010","doi-asserted-by":"crossref","first-page":"57369","DOI":"10.1109\/ACCESS.2025.3554744","article-title":"Lung-AttNet: An Attention Mechanism-Based CNN Architecture for Lung Cancer Detection With Federated Learning","volume":"13","author":"C Saha","year":"2025","journal-title":"IEEE Access"},{"key":"pone.0351762.ref011","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 770\u20138. https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"pone.0351762.ref012","article-title":"Explainable AI framework utilizing advanced deep learning for lung disease classification. arXiv preprint","author":"S Ifty","year":"2024"},{"key":"pone.0351762.ref013","article-title":"Automatic detection and multi-class classification of COVID-19, pneumonia, and tuberculosis diseases in chest X-ray images using deep learning techniques","author":"MAH Metwally","year":"2022"},{"key":"pone.0351762.ref014","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2018. 4510\u201320. https:\/\/doi.org\/10.1109\/cvpr.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"pone.0351762.ref015","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":"GMM Alshmrani","year":"2023","journal-title":"Alexandria Engineering Journal"},{"key":"pone.0351762.ref016","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), 2017. 618\u201326. https:\/\/doi.org\/10.1109\/iccv.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"pone.0351762.ref017","doi-asserted-by":"crossref","first-page":"106646","DOI":"10.1016\/j.compbiomed.2023.106646","article-title":"High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images","volume":"155","author":"FJM Shamrat","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0351762.ref018","doi-asserted-by":"crossref","first-page":"100460","DOI":"10.1016\/j.dajour.2024.100460","article-title":"A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images","volume":"11","author":"S Hamal","year":"2024","journal-title":"Decision Analytics Journal"},{"key":"pone.0351762.ref019","doi-asserted-by":"crossref","first-page":"100458","DOI":"10.1016\/j.dajour.2024.100458","article-title":"An efficient multi-stage ensemble deep learning framework for diagnosing infectious diseases","volume":"11","author":"RK Bondugula","year":"2024","journal-title":"Decision Analytics Journal"},{"issue":"7","key":"pone.0351762.ref020","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14380","article-title":"DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification","volume":"25","author":"H Tian","year":"2024","journal-title":"J Appl Clin Med Phys"},{"key":"pone.0351762.ref021","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2261\u20139. https:\/\/doi.org\/10.1109\/cvpr.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"pone.0351762.ref022","doi-asserted-by":"crossref","first-page":"106156","DOI":"10.1016\/j.compbiomed.2022.106156","article-title":"Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI","volume":"150","author":"M Bhandari","year":"2022","journal-title":"Comput Biol Med"},{"key":"pone.0351762.ref023","unstructured":"Fernando 2 R. X-ray lung diseases images (9 classes). 2023. https:\/\/www.kaggle.com\/datasets\/fernando2rad\/x-ray-lung-diseases-images-9-classes"},{"key":"pone.0351762.ref024","unstructured":"Siddhartha M, Santra A. COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19. 2020. https:\/\/arxiv.org\/abs\/2006.13873"},{"issue":"1","key":"pone.0351762.ref025","doi-asserted-by":"crossref","DOI":"10.1007\/s44163-024-00110-x","article-title":"A multiclass deep learning algorithm for healthy lung, Covid-19 and pneumonia disease detection from chest X-ray images","volume":"4","author":"G Mohan","year":"2024","journal-title":"Discov Artif Intell"},{"issue":"19","key":"pone.0351762.ref026","doi-asserted-by":"crossref","first-page":"9289","DOI":"10.3390\/app11199289","article-title":"Multi-Class Classification of Lung Diseases Using CNN Models","volume":"11","author":"M Hong","year":"2021","journal-title":"Applied Sciences"},{"issue":"1","key":"pone.0351762.ref027","doi-asserted-by":"crossref","first-page":"19373","DOI":"10.1038\/s41598-023-46147-3","article-title":"Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images","volume":"13","author":"MH Al-Sheikh","year":"2023","journal-title":"Sci Rep"},{"key":"pone.0351762.ref028","first-page":"1","article-title":"Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm","author":"AMQ Farhan","year":"2023","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"pone.0351762.ref029","doi-asserted-by":"crossref","first-page":"915","DOI":"10.3390\/diagnostics12040915","article-title":"Deep Learning in Multi-Class Lung Diseases\u2019 Classification on Chest X-ray Images","volume":"12","author":"S Kim","year":"2022","journal-title":"Diagnostics (Basel)"},{"key":"pone.0351762.ref030","doi-asserted-by":"crossref","first-page":"124062","DOI":"10.1109\/ACCESS.2024.3454537","article-title":"Multi-Label Classification of Lung Diseases Using Deep Learning","volume":"12","author":"M Irtaza","year":"2024","journal-title":"IEEE Access"},{"issue":"7","key":"pone.0351762.ref031","doi-asserted-by":"crossref","first-page":"280","DOI":"10.3390\/a17070280","article-title":"LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications","volume":"17","author":"R Aldamani","year":"2024","journal-title":"Algorithms"},{"key":"pone.0351762.ref032","doi-asserted-by":"crossref","first-page":"106114","DOI":"10.1016\/j.bspc.2024.106114","article-title":"A novel NASNet model with LIME explanability for lung disease classification","volume":"93","author":"T Venkata Krishnamoorthy","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"pone.0351762.ref033","first-page":"630","article-title":"Identity Mappings in Deep Residual Networks","volume-title":"Lecture Notes in Computer Science","author":"K He","year":"2016"},{"key":"pone.0351762.ref034","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. 2818\u201326.","DOI":"10.1109\/CVPR.2016.308"},{"key":"pone.0351762.ref035","doi-asserted-by":"crossref","unstructured":"Sohel A, Rahman MM, Zubaer T, Rifat RI, Majumder PP, Bijoy MdHI. Improving Pneumonia Detection in X-ray Images with Hybrid Deep Learning Techniques. In: 2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI), 2024. 1\u20136. https:\/\/doi.org\/10.1109\/sti64222.2024.10951093","DOI":"10.1109\/STI64222.2024.10951093"},{"key":"pone.0351762.ref036","unstructured":"Radiopaedia. Normal chest X-ray. https:\/\/radiopaedia.org\/cases\/normal-chest-x-ray-1 2023. 2026 March 4."},{"key":"pone.0351762.ref037","unstructured":"Mooney P. Chest X-Ray Images (Pneumonia). https:\/\/www.kaggle.com\/datasets\/paultimothymooney\/chest-xray-pneumonia 2018. 2026 March 4."},{"key":"pone.0351762.ref038","unstructured":"TrainingDataPro. Chest X-Ray 17 Diseases. https:\/\/www.kaggle.com\/datasets\/trainingdatapro\/chest-xray-17-diseases 2026 March 4."},{"key":"pone.0351762.ref039","unstructured":"Bansal NM. Emphysema Dataset from NIH Chest X-Ray. https:\/\/www.kaggle.com\/datasets\/neelmbansal\/emphysema-dataset-from-nih-chest-x-ray. 2026 March 4."},{"key":"pone.0351762.ref040","unstructured":"Rahman T. Tuberculosis (TB) Chest X-Ray Dataset. https:\/\/www.kaggle.com\/datasets\/tawsifurrahman\/tuberculosis-tb-chest-xray-dataset?resource=download 2026 March 4."},{"key":"pone.0351762.ref041","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Carpio AB, Bojorges-Valdez E, Y\u00e1\u00f1ez-Suarez O. Pipeline System for Versatile Medical Image Processing. In: 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 2023. 153\u20134. https:\/\/doi.org\/10.1109\/ieeeconf58974.2023.10404838","DOI":"10.1109\/IEEECONF58974.2023.10404838"},{"key":"pone.0351762.ref042","doi-asserted-by":"crossref","unstructured":"Gutta R. RespiraAI: Novel Elevated Feature Recalibration Convolutional Neural Network for Efficient Diagnosis and Classification of Pulmonary Diseases. In: 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2025. 1\u20135. https:\/\/doi.org\/10.1109\/iatmsi64286.2025.10984767","DOI":"10.1109\/IATMSI64286.2025.10984767"},{"key":"pone.0351762.ref043","doi-asserted-by":"crossref","unstructured":"Wang H, Wang Z, Du M, Yang F, Zhang Z, Ding S, et al. Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020. 111\u20139. https:\/\/doi.org\/10.1109\/cvprw50498.2020.00020","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"pone.0351762.ref044","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C. Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 1135\u201344.","DOI":"10.1145\/2939672.2939778"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:35:53Z","timestamp":1781717753000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351762"}},"subtitle":[],"editor":[{"given":"Asadullah","family":"Shaikh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,6,17]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6,17]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0351762","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,17]]}}}