Horse Herd Optimization with Gate Recurrent Unit for an Automatic Classification of Different Facial Skin Disease

AbstractThe human body ’s largest organ is the skin which covers the entire body. The facial skin is one area of the body that needs careful handling. It can cause several facial skin diseases like acne, eczema, moles, melanoma, rosacea, and many other fungal infections. Diagnosing these diseases has been difficult due to challenges like the high cost of medical equipment and the lack of medical competence. However, various existing systems are utilized to detect the type of facial skin disease, but those approaches are time-consuming and inaccurate to detect the disease at early stages. To address various issues, a deep learning-based gate recurrent unit (GRU) has been developed. Non-linear diffusion is used to acquire and pre-process raw pictures, adaptive histogram equalization (AHE) and high boost filtering (HBF). The image noise is removed by using non-linear diffusion. The contrast of the image is maxi mized using AHE. The image’s edges are sharpened by using HBF. After pre-processing, textural and colour features are extracted by applying a grey level run-length matrix (GLRM) and chromatic co-occurrence local binary pattern (CCoLBP). Then, appropriate features are selected using horse herd opti mization (HOA). Finally, selected features are classified using GRU to identify the types of facial skin disease. The proposed model is investigated using the Kaggle database that consists of different face skin disease images such as rosacea, eczema, basal cell carcinoma, a...
Source: Journal of Digital Imaging - Category: Radiology Source Type: research