International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Review on Deep Learning-Based Lung Disease Detection Using Chest X-Ray Images |
|---|---|
| ABSTRACT | Lung diseases such as pneumonia and tuberculosis, along with occupational conditions like silicosis and pneumoconiosis, remain a major global health burden, particularly in regions with limited access to expert radiologists. Traditional chest X-ray interpretation is time-consuming, depends on specialist availability, and is prone to inter-observer inconsistency. This paper presents a narrative review of how convolutional neural networks (CNNs), strengthened by transfer learning, are being applied to automated multi-class lung disease detection from chest X-ray images. We survey the prominent public datasets, the established architectures (VGGNet, ResNet, DenseNet, and EfficientNet), and the growing role of explainable AI (XAI) through Grad-CAM and LIME. We give particular attention to occupational lung diseases, where data scarcity is the main obstacle to progress. Drawing the threads of the review together, we also outline a modular, web-based reference architecture for deployment in resource-constrained healthcare settings; this is a conceptual design rather than an implemented system. Finally, we discuss the ethical questions around automation, equity, and data privacy. Our analysis suggests that although benchmark performance has reached radiologist-level accuracy for common conditions, the reported numbers should be read with caution, and real-world deployment still demands careful attention to domain shift, regulatory compliance, and clinical workflow integration. |
| AUTHOR | JOEL JACOB VARGHESE, NAVIN CHOLANGI, MANCHIT CHOUDHARY, MANNA SARA BILU, DR. YOGESH GOLHAR Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, India Assistant Professor, Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406087 |
| pdf/87_A Review on Deep Learning-Based Lung Disease Detection Using Chest X-Ray Images.pdf | |
| KEYWORDS | |
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