Executive Summary : | Chronic Obstructive Pulmonary Disease (COPD) is a prevalent medical condition, with over 100 million patients diagnosed early. Traditional methods, such as CT scans, are costly and time-consuming for accurate diagnosis and classification. Misdiagnosis in clinical practice is common due to similar symptoms of some diseases. This project proposes a multimodal framework for early diagnosis and accurate prediction of COPD using artificial intelligence. The system extracts discriminatory features from computerized tomography (CT) chest X-images and cough samples datasets collected from the All India Institute of Medical Sciences (AIIMS) Raipur and open respiratory sound and CT scan datasets. The CT diagnostic model and cough model will extract features and integrate them using deep multimodal fusion techniques for early diagnosis, prediction, and severity assessment of COPD patients. The project aims to provide early diagnostic solutions to improve livelihood opportunities and quality of life for COPD patients in tribal areas in Chhattisgarh through sustainable growth in health and production in tribal forest regions. Real-time monitoring diagnosis solutions will be deployed in tribal and remote regions across Chhattisgarh and across the state. Early diagnosis android solutions will be deployed in smart villages and remoted areas in India. |