Enhancing Medical Visual Question and Answering System

This project tackles fundamental weaknesses of the current versions of the Medical VQA System: namely, low accuracy in the interpretation of complex medical images and ambiguity in query handling. Its developed version, built with CNNs and RNNs, tends to be highly dependent on heavy labeled datasets with difficulties in data processing of high dimensionality, so it has very limited applicability in clinical settings. Our proposed system, integrating ManualNet, GoogLeNet, and MobileNet, would extract more specific features to be utilized with the Transformer-based models including BERT and GPT for the advanced question processing. The application of attention mechanisms along with data augmentation increases the alignment between vision and text and even better in dealing with the problem of data scarcity. This solution should enhance accuracy, adaptability, and efficiency in Medical VQA systems which ensure more reliable diagnostic support and therefore better care for patients. Thus, this project exhibits an extremely robust AI-driven approach toward solving the current challenges and progression of the practical application of Medical VQA in the healthcare field.