| Scholar Name | Mamta Patel |
| Department | Instrumentation & Control |
| Reseach Area | Deep Learning |
| Supervisor Name | Dr. M.K.Shah |
| Title | Decision Support System for Lung Covid 19 using deep learning |
| Abstract | The Convolutional Neural Network (CNN) has proven to be a highly effective architecture in computer vision applications for its ability to directly extract and learn intricate features from image data. CNNs, a key architecture of deep learning, have revolutionized medical image analysis by providing precise, automated diagnostic capabilities through the learning of complex and intricate patterns from medical imagery with minimal human intervention. The innovation in DL has significantly accelerated and improved the accuracy of diagnosis, empowered healthcare professionals to deliver superior patient care. CNNs have shown exceptional proficiency in tasks such as disease identification, categorization, and segmentation, making them indispensable tools in modern medical diagnostics. The COVID-19 pandemic, which emerged in 2019, highlighted different lung related disorders as the critical need for timely and accurate diagnoses to minimize mortality. The common lung diseases, such as pneumonia, lung opacity, tuberculosis pose serious health challenges globally and demand for the early detection for the proper treatment planning. Chest X-ray (CXR) imaging is a widely utilized diagnostic tool due to its rapid, accurate, and cost-effective nature. However, the interpretation of chest radiographs presents challenges due to the significant similarities among various lung disorders, including pneumonia, and COVID-19, potentially leading to diagnostic errors. This challenge encourages the need for decision support systems (DSS) designed with advanced computational models to assist clinicians by providing enhanced diagnostic precision, reducing errors, and improving disease differentiation. The usage of deep learning models in lung image analysis can reliably distinguish between similar pathologies, facilitating more accurate and timely medical interventions. This proposed research work presents usage of CNNs with different deep learning approaches to achieve multi-class classification of lung diseases and predict severity sc |