A Hybrid System for Automated Diagnosis of Inflammatory Skin Diseases: Neural Networks and Survey-Based Prediction

This article presents an integrated system for automated diagnosis, combining convolutional neural networks (CNNs) for dermatological image analysis with a patient survey for clinical data collection. The goal is to evaluate the effectiveness of this hybrid approach compared to traditional diagnostic methods. The system was tested on a curated dataset composed of images collected from DermNet and publicly available dermatological image databases. The results demonstrate high diagnostic accuracy for inflammatory skin diseases, with the combined approach outperforming standalone methods. These findings highlight the potential of integrating machine learning with patient-reported data to enhance dermatological diagnostics. The proposed system can be implemented in clinical practice and integrated into existing medical platforms, aiding dermatologists in decision-making and improving patient care. Future research will focus on expanding the system to diagnose a broader range of skin conditions and incorporating additional clinical data to enhance its performance.