A Comprehensive Review of Transfer Learning Architype for An Enhanced Melanoma Skin Cancer Using Hybrid Texture Features Detection and Classification Scheme in Medical Image Processing

Abstract

Melanoma skin cancer remains one of the most aggressive forms of dermatological malignancies, necessitating early detection and precise classification for improved patient outcomes. Recent advancements in medical image processing and deep learning have significantly enhanced diagnostic capabilities, particularly through transfer learning paradigms. This paper presents a comprehensive review of transfer learning architectures integrated with hybrid texture feature extraction techniques for melanoma detection and classification. The study explores how pre-trained convolutional neural networks, when combined with handcrafted features such as Local Binary Patterns, Gray-Level Co-occurrence Matrix, and wavelet transforms, contribute to improved accuracy and robustness. Emphasis is placed on hybrid frameworks that leverage both deep and traditional features to address challenges such as limited annotated datasets, class imbalance, and variability in lesion appearance. Furthermore, this review highlights the role of fine-tuning strategies, feature fusion mechanisms, and optimization techniques in enhancing classification performance. The paper also examines benchmark datasets, evaluation metrics, and recent methodological trends in melanoma diagnosis. By synthesizing findings from recent studies, this work provides insights into the effectiveness of hybrid transfer learning approaches and identifies potential research directions for future developments in automated skin cancer detection systems. The integration of advanced computational techniques with clinical workflows is expected to significantly improve early diagnosis and reduce mortality rates associated with melanoma.