A Hybrid model for skin classification using Neural Networks and K-Nearest Neighbors

. Globally common skin diseases require early detection to reduce serious complications and improve treatment results. Current diagnostic systems operate using traditional methods that frequently produce errors and show inconsistent results which emphasizes the necessity for improved diagnostic technologies. The research unveils DermAID which uses Convolutional Neural Networks (CNNs) within a multi-modal system to automate initial skin dis- ease detection. The system combines user-provided skin images with symptom questionnaires to enhance diagnostic precision. The study used 19,500 images from 23 dermatological categories including conditions like acne and melanoma which came from DermNet and processed those images with resizing, normalization, augmentation, and standardization before training robust CNN and KNN models For feature extraction and disease classification the architecture integrates convolutional layers with pooling layers and ReLU activation functions and fully connected layers. Through a focus on interpretability and generalizability this system effectively manages biases and accounts for real-world variations in dermatology datasets. The methodology shows AI potential for quicker diagnostics and cost savings in skin disease management especially where Mumbai’s climate makes skin conditions more common despite quantitative details missing from the results. The research encourages AI-driven early intervention strategies which supply users with self-assessment capabilities and assist clinical operations during shortages of dermatologists. The paper examines DermAID’s scalability as a global dermatological solution while addressing model transparency limitations and future improvement strategies.