Dual-Stage Attention MobileNetV2 with Enhanced Arctic Belief Network for Accurate Skin Lesion Segmentation and Classification

One of the most prevalent and deadly cancers is skin cancer, and early diagnosis of the disease has a significant chance of increasing survival. The traditional methods of diagnosis are clinically satisfactory but have limitations such as high costs, prolonged processing time, and inaccessibility in remote areas. The proposed research outlines a novel deep learning model, Extreme Elman Spike Learning-Driven Deep Arctic Belief Neural Network (EESL-DABNN), in particular, to ensure efficient and successful skin lesion segmentation and classification. It is built on multistage processing where sharpening of images by using fast Kuwahara mean filtering is used as the first stage; then histogram equalization and normalization are used to ensure that the intensity of the pixel remains constant. Rotation, flipping, and brightness adjustments augmentation techniques enhance generalization and overcome the imbalance in the dataset. Feature extraction involves the use of a Dual-Stage Attention MobileNetV2 with large-small kernel convolution to learn local and global features. A residual coordinate dual-encoder fusion attention mechanism is also implemented to add more spatial-perception and channel-perception. Segmentation and classification are done using a lightweight EESL-DABNN, optimized through the Arctic Puffin Optimization algorithm for adaptive search space exploration. The experimental outcomes on the ISIC, Melanoma Detection and HAM1000 datasets have accuracies of 99.92%, 99.90%, and 99.94%, respectively, which are more diagnostically reliable at a low cost of computation in clinical applications at scale.