Hybrid Feature Extraction and Deep Learning for Improved Melanoma Diagnosis from Skin Lesion Images

The study emphasizes the need for improved melanoma diagnosis by overcoming the limitations of existing techniques. Conventional approaches, which focus on manual feature engineering or standalone deep learning, must precisely capture subtle melanoma patterns. The proposed hybrid System combines traditional image processing with advanced feature extraction and Convolutional Neural Network (CNN) to solve that. The methodology begins with extensive data preprocessing to ensure standardized input quality. Hybrid feature extraction combines edge recognition, texture analysis, and color histogram computing to capture a wide range of low and high-level features important for melanoma diagnosis. The CNN architecture is intended explicitly for melanoma classification, utilizing the power of convolutional layers for feature learning and fully linked layers for classification. The results show that the proposed System outperforms existing systems, with 99.1% accuracy, 98.3% precision, 96% recall, and 96.5% F1 Score in computational metrics. The System's performance is highlighted in comparison analysis, underlining its potential as an advanced diagnostic tool in dermatology, ultimately contributing to improved clinical results and patient care.