Multi-Class Deep Learning-Based Enhanced Image Segmentation and Feature Extraction Technique for Early Classification of Skin Diseases

Abstract

Skin disorders pose great health issues among millions of people across the globe and thus require accurate diagnosis and treatment. This research article proposed an enhanced deep learning-based image segmentation and feature extraction approach for the early categorization of human skin diseases utilizing the Dermnet dataset of image samples classified under 23 categories. Input images are applied during the preprocessing stage to an enhanced image segmentation technique known as enhanced holistically nested edge detection (EHNED) toward detail enrichment in edges.  Further, for multiple feature extraction, the model used EfficientNet-B0 and extracted 1280 features from an image represented as a 1D array. The extracted features form a baseline for training and testing CNNs on the diagnosis of skin diseases. It tries to bridge all the loopholes from previous research by observing an extremely vast and heterogeneous dataset and uses better techniques for segmentation and feature extraction. The proposed framework is likely to increase the recall performance metric to 91%, specificity to 96%, and ability to enable early diagnosis in case of skin diseases. This enhancement is supposed to follow more effective management and better patient outcomes. Findings in this study are likely to contribute toward dermatology research and promote intelligent systems in diagnostics for automatic classification of skin diseases.