A Survey of Methods and Architectures for Transfer Learning Archetype for An Enhanced Melanoma Skin Cancer Using Hybrid Texture Features Detection and Classification Scheme in Medical Image Processing

Abstract

Diabetic foot ulcers (DFUs) represent one of the most severe Melanoma skin cancer is one of the most aggressive forms of skin malignancy, requiring early and accurate diagnosis to improve survival rates. Recent advancements in medical image processing have significantly enhanced automated melanoma detection through the integration of deep learning and transfer learning techniques. This paper presents a comprehensive survey of methods and architectures that utilize transfer learning paradigms combined with hybrid texture feature extraction for improved melanoma classification. The study emphasizes the importance of leveraging pretrained convolutional neural networks to overcome data scarcity and enhance feature generalization. Furthermore, hybrid approaches that combine deep features with handcrafted texture descriptors such as Local Binary Patterns and Gray-Level Co-occurrence Matrices are explored for their effectiveness in capturing both global and local image characteristics. The survey analyzes various datasets, preprocessing strategies, model architectures, and evaluation metrics used in contemporary research. It also highlights key challenges such as class imbalance, overfitting, and domain adaptation issues. The findings suggest that hybrid feature-based transfer learning models consistently outperform standalone approaches in terms of accuracy, sensitivity, and specificity. This work aims to provide a structured overview for researchers and practitioners to better understand the current landscape and identify future research directions in melanoma detection using advanced machine learning techniques.