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Automated detection and diagnosis of pulmonary diseases such as pneumonia, tuberculosis, lung cancer, and COVID-19 play a crucial role in improving patient outcomes and reducing healthcare burdens, especially in the context of the ongoing global pandemic. In this research, we propose a deep learning-based approach for the accurate and efficient detection of these diseases from medical imaging data. Leveraging convolutional neural networks (CNNs) and advanced image processing techniques, we develop models capable of analyzing chest X-rays and CT scans to identify pathological features indicative of pneumonia, tuberculosis, lung cancer, and COVID-19. Through rigorous experimentation and optimization, we achieve high sensitivity and specificity in disease detection, addressing key challenges such as data scarcity, model interpretability, and integration into clinical workflows. Evaluation on diverse datasets and real-world clinical scenarios demonstrates the clinical utility and feasibility of our approach, paving the way for its adoption in healthcare practice. Our findings contribute to advancing the field of medical image analysis and hold promise for improving diagnostic accuracy and patient care in pulmonary medicine, particularly in the context of the COVID-19 pandemic.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Y.Tejaswini, tejaswiniy@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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