International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com Y.Tejaswini 7 2 (March - April) 2025 03 March-April enDeep LearningCNNsMedical ImagingPneumoniaTuberculosisLung CancerCOVID-19 Detection Automated detection and diagnosis of pulmonary diseases such as pneumonia tuberculosis lung cancer and COVID19 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 learningbased 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 Xrays and CT scans to identify pathological features indicative of pneumonia tuberculosis lung cancer and COVID19 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 realworld 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 COVID19 pandemic Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2025 All Rights Reserved