Deep Neural Network for Automated Medical Diagnosis and Treatment Recommendation System

The proposed project aims to develop a comprehensive full-stack application that leverages advanced technologies to address two distinct healthcare challenges. The first use case involves predicting diseases based on user prompts using natural language processing (NLP) powered by Beijing embeddings and the AI21 Jurassic LLM. Drawing insights from Siddha medicine and medical student books, the NLP model is trained to interpret user queries and provide accurate disease predictions. The integration of this model into a FastApi-based backend facilitates seamless communication with a user-friendly HTML and CSS frontend. Simultaneously, the second use case focuses on the classification of lung and skin cancers through image analysis. Leveraging convolutional neural networks (CNNs), the application classifies X-ray and microscope images to identify potential malignancies. The FastApi backend handles image uploads and communicates with the image classification model, ensuring a cohesive and responsive user experience. The frontend, designed with HTML and CSS, allows users to intuitively interact with the application by submitting medical images for analysis. Both use cases underscore the project's commitment to merging traditional medical knowledge with cutting-edge technologies for improved diagnostic capabilities. The incorporation of Siddha medicine insights, combined with state-of-the-art NLP and image classification models, enhances the accuracy and depth of disease predictions. The deployment of secure and scalable practices ensures user data integrity and facilitates future expansion. This project not only addresses critical healthcare challenges but also showcases the potential of interdisciplinary approaches, combining natural language understanding and image analysis.