A Revolutionary Transfer Learning System for Multimodal Skin Lesion Evaluation

The early detection of dermatological conditions, particularly skin cancer, relies on the accurate classification of skin lesions. Traditional methods relying on single-modality images often fail to provide comprehensive diagnostic accuracy. This research introduces a novel transfer learning framework that integrates Vision Transformers(ViT),convolutional neural networks(CNN), and feature extraction techniques with multi-modal data, including macroscopic dermoscopic images and patient metadata. The study employs models such as MobileNetV2, ResNet152V2, VGG16, and Xception, enhanced with classifiers like support vector machine(SVM), K-nearst neighbour (KNN), and a Voting Classifier combined with Random Forest(RF) and Decision Tree(DT). The Xception model achieves 100% accuracy when combined with feature extraction and ensemble classification. Additionally, advanced detection models, including YOLOv5x6, YOLOv8, and YOLOv9, enhance lesion identification, ensuring precise and efficient skin condition analysis. The integration of a channel attention mechanism and region of interest (ROI) techniques further improves feature extraction, leading to enhanced diagnostic performance. The proposed approach demonstrates significant improvements over existing methods, addressing challenges in lesion segmentation and multimodal data limitationsultimately contributing to better patient outcomes in dermatology.