AI-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction

Abstract

An AI driven healthcare system uses deep learning techniques to enhance disease identification and medical picture analysis. The system's systematic workflow includes data collection, preprocessing, model training and evaluation. Multiple deep learning architectures are used to accurately classify and segment medical images such as ResNet50, VGG-16, and U-Net. The methodology takes advantage of the techniques of optimization, transfer learning and data augmentation to improve model performance. It covers several classification tasks, which enables you to find patterns in photos to perform efficient classification. The critical performance measures such as accuracy, precision, recall and F1 score are used to assess the system to ensure dependability and effectiveness of the predictive analysis. The framework provides an effective and scalable approach for automating the diagnostic and decision making support through utilization of big datasets and refinement of computational model.

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