AI-Based Hybrid Model for Detection of Multiple Skin Diseases Using Dermoscopy Images

Abstract

Skin diseases are among the most common health conditions, and their early and accurate diagnosis is essential for effective treatment and improved patient outcomes. Conventional diagnosis of dermatological conditions primarily relies on visual examination and the expertise of dermatologists, which can be time-consuming and subject to inter-observer variability. This study proposes an AI-based hybrid model for the detection and classification of multiple skin diseases using dermoscopy images. The proposed approach combines advanced image preprocessing techniques with deep learning and machine learning methods to extract meaningful visual features and improve classification performance. Dermoscopic images are processed to reduce noise, enhance lesion characteristics, and normalize image quality before being analyzed by the hybrid model. Deep convolutional neural networks are employed for automated feature extraction, while complementary machine learning techniques are integrated to improve the discrimination of visually similar skin conditions. The model is designed to classify multiple dermatological diseases and distinguish abnormal lesions from healthy skin. Performance is evaluated using standard metrics such as accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the ROC curve. By reducing dependence on manual interpretation and providing rapid, consistent predictions, the proposed system has the potential to support dermatologists in early screening and clinical decision-making. The study demonstrates the potential of hybrid artificial intelligence techniques as an efficient and scalable computer-aided diagnostic solution for multi-class skin disease detection from dermoscopy images.