Multi-Disease Diagnosis Using Medical Images

This work presents a novel approach for disease diagnosis using medical imaging modalities—MRI, X-ray, and CT scans—to classify heart, brain, and lung disorders. Leveraging "Deep learning" techniques, particularly "Convolutional Neural Networks" (CNNs), this work focuses on accurately identifying anomalies within these organs. A diverse dataset is prepared, encompassing varied cases of cardiovascular, neurological, and pulmonary conditions, enabling the model's training and validation. The methodology involves collecting images, employing transfer learning, and implementing data augmentation techniques to enhance the model's robustness. Evaluation metrics including accuracy, sensitivity, specificity, and AUC demonstrate the model's efficacy in differentiating between disease categories and healthy instances. This research contributes to advancing healthcare by providing a tool for precise and timely diagnosis, potentially improving patient outcomes across these critical medical domains.