Detecting Diseases Using Chest X-Ray Images and Pre-Trained Machine Learning Models

Dr. Nadiminti Venkata Ramana Murty, M. Anil

Abstract: Chest X-ray imaging is an essential diagnostic tool for identifying various thoracic diseases such as pneumonia, tuberculosis, and COVID-19. The advent of deep learning, particularly pre-trained convolutional neural networks (CNNs), has significantly improved the automation and accuracy of disease detection in medical imaging. This paper investigates the use of several state-of-the-art pre-trained CNN architectures for detecting diseases from chest X-ray images. The models are fine-tuned and evaluated on publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, and F1-score are analyzed to demonstrate the effectiveness of transfer learning for medical diagnosis.

Keywords: Chest X-ray, Deep Learning, Pre-trained Models, Disease Detection, Transfer Learning, Convolutional Neural Networks

How to Cite?: Dr. Nadiminti Venkata Ramana Murty, M. Anil, "Detecting Diseases Using Chest X-Ray Images and Pre-Trained Machine Learning Models", Volume 14 Issue 10, October 2025, International Journal of Science and Research (IJSR), Pages: 317-318, https://www.ijsr.net/getabstract.php?paperid=SR25923155811, DOI: https://dx.doi.org/10.21275/SR25923155811

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