Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Abstract— Pneumonia requires timely diagnosis to reduce mortality, but manual chest X-ray interpretation is subjective to human bias. This study leverages convolutional neural networks—EfficientNetV2B0 for pneumonia detection and VGG16 for bacterial-viral classification—trained on publicly available datasets using data augmentation, transfer learning, and class balancing techniques. The models achieve 99.45% and 97.45% accuracy, respectively, with strong performance across evaluation metrics. Grad-CAM visualization enhances interpretability by highlighting key anatomical regions influencing predictions. These findings reinforce the potential of AI in assisting radiologists with pneumonia diagnosis while improving accessibility for patients in remote areas, enabling clearer understanding of medical insights beyond clinical settings.
"A Deep Learning-Based Pneumonia Detection and Classification System Using Chest X-Ray Images", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b1-b5, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504101.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator