Authors - Kruthiga S, Sindhu Chandra Sekharan, H.Summia Parveen, C. Kavitha, S. Umamaheswari Abstract - The transformative potential of deep learning techniques to revolutionize the landscape of medical image analysis, enabling accurate and efficient multi-disease prediction across a spectrum of critical health conditions. This work provides a solution to the early detection challenge of disease through prediction for Tuberculosis, Pneumonia, Glaucoma, and Brain Tumors using deep learning methods. By leveraging the expressive power of convolutional neural networks and transfer learning strategies, we have developed a robust framework capable of learning intricate patterns and subtle features indicative of diseases such as brain tumor, glaucoma, pneumonia, and tuberculosis. Through meticulous data preprocessing, model selection, and rigorous training and validation procedures, our approach ensures the reliability and generalizability of disease predictions, offering clinicians a powerful tool for early diagnosis and personalized treatment planning. The integration of Python programming language facilitates seamless implementation and deployment of our framework, making it accessible to healthcare practitioners and researchers alike. Overall, our study represents a significant advancement in the field of medical image analysis, with the potential to improve patient outcomes and revolutionize healthcare delivery on a global scale.
