MediScan : A Robust Xception-Based Skin Disease Classification System with Blockchain Integration

This research aims to revolutionize the prediction of skin diseases in patients by employing advanced deep learning methodologies. By integrating transfer learning with Xception and Convolutional Neural Network (CNN) models, we develop a robust predictive framework. Transfer learning is leveraged to accelerate training and improve accuracy by using pre-trained models on patient-specific data. To address data security concerns, we incorporate blockchain technology to securely store patient data, ensuring transparency and maintaining integrity. The model's efficacy is demonstrated on a comprehensive skin disease image dataset comprising over 3,396 images across 6 different skin conditions. Results show an improvement in diagnostic accuracy to 85%, a significant enhancement over traditional methods. This integration of transfer learning, advanced CNN models, and blockchain establishes a framework that enhances dermatological diagnostics and ensures a secure, transparent ecosystem for managing patient health data, setting new standards in predictive analytics.