Artificial Intelligence Techniques for Transfer Learning Architype for An Enhanced Melanoma Skin Cancer Using Hybrid Texture Features Detection and Classification Scheme in Medical Image Processing: Trends and Challenges

Abstract

Melanoma skin cancer remains one of the most aggressive and life-threatening forms of dermatological malignancies, where early and accurate diagnosis plays a critical role in improving patient survival rates. In recent years, artificial intelligence techniques, particularly deep learning and transfer learning architectures, have demonstrated significant potential in enhancing melanoma detection and classification. This study presents a comprehensive review of artificial intelligence techniques focusing on transfer learning archetypes integrated with hybrid texture feature extraction for improved melanoma diagnosis. The proposed framework explores the synergy between pretrained convolutional neural networks and handcrafted texture descriptors such as Local Binary Patterns, Gray Level Co-occurrence Matrix, and wavelet-based features to improve classification accuracy. The study highlights recent trends in hybrid feature fusion, domain adaptation, and optimization strategies that enhance generalization across diverse dermoscopic datasets. Furthermore, it identifies key challenges including data imbalance, model interpretability, computational complexity, and clinical integration barriers. The review also emphasizes the importance of explainable AI and robust validation protocols in real-world medical applications. The findings suggest that hybrid approaches combining deep transfer learning with traditional feature engineering significantly outperform standalone models, paving the way for more reliable and scalable melanoma detection systems in medical image processing.