A TWO-STAGE DEEP LEARNING FRAMEWORK FOR PNEUMONIA DIAGNOSIS FROM CHEST RADIOGRAPHS WITH GRAD-CAM-BASED INTERPRETABILITY

A TWO-STAGE DEEP LEARNING FRAMEWORK FOR PNEUMONIA DIAGNOSIS FROM CHEST RADIOGRAPHS WITH GRAD-CAM-BASED INTERPRETABILITY | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on A TWO-STAGE DEEP LEARNING FRAMEWORK FOR PNEUMONIA DIAGNOSIS FROM CHEST RADIOGRAPHS WITH GRAD-CAM-BASED INTERPRETABILITY Author : Pranesh Sathish Kumar 0009-0000-3634-6554 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176463714.47046170/v1 187 views 156 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Pneumonia is a leading cause of death globally, especially in areas with limited access to radiological expertise. This study introduces an advanced deep learning approach for the automated detection of pneumonia using chest X-ray images, employing the VGG16 convolutional neural network (CNN) for high-accuracy classification. The model was trained on a diverse dataset comprising various pneumonia subtypes, including COVID-19, viral pneumonia, bacterial pneumonia, and normal cases, providing a comprehensive solution for accurate diagnosis across different conditions. To further enhance the model's usability, Grad-CAM visualizations were incorporated, offering transparent and interpretable heatmaps that highlight the affected regions within the lung. A web-based platform was also developed to facilitate real-time analysis, allowing medical professionals and researchers to remotely access predictions and visual insights for improved clinical decision-making. The results show the potential of DL-assisted diagnostics to streamline pneumonia detection, reduce radiologist workload, and improve accessibility to critical medical imaging analysis, ultimately enhancing healthcare delivery, particularly in resource-restricted regions. Supplementary Material File (rdzmfvwbjddfhrskhfwznwnvscnhdtkk.pdf) Download 2.81 MB Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords convolutional neural network(cnn) deep learning grad-cam pneumonia detection Authors Affiliations Pranesh Sathish Kumar 0009-0000-3634-6554 [email protected] Alliance Academy for Innovation Cumming View all articles by this author Metrics & Citations Metrics Article Usage 187 views 156 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Pranesh Sathish Kumar. A TWO-STAGE DEEP LEARNING FRAMEWORK FOR PNEUMONIA DIAGNOSIS FROM CHEST RADIOGRAPHS WITH GRAD-CAM-BASED INTERPRETABILITY. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176463714.47046170/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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