AUTOMATIC CLASSIFICATION OF SEVERITY STAGES OF PLAQUE PSORIASIS BY USING IMAGE PROCESSING

Abstract

Psoriasis is a common skin disease affecting all ages and sexes and is characterized by itchy, scaly patches most commonly on the knees, elbows, trunk, and scalp. The most common type is plaque psoriasis, which causes raised, red patches on the skin that are covered with a silvery-white buildup of dead skin cells, based on its severity which has three stages: mild, moderate, and severe. Imaging technology has revolutionized diagnosis and treatment. Thus, this study is aimed at an automatic classification of the severity stages of plaque psoriasis by using the Artificial Neural Network technique. In total, 200 sample plaque psoriasis images were taken from Bisidimo General Hospital, Oromia region, and Yem Dermatology and Venereology clinic, Harar. The sample images were acquired using a Samsung Galaxy A14 mobile camera with (1080 × 2408) resolution and loaded or imported to a computer. The imported images were pre-processed like converting from RGB to gray-scale image and enhanced using CLAHE to increase the visibility of the plaque psoriasis images. Then, thresholding was used to segment the plaque psoriasis-diseased images, and the morphological feature was extracted from each image. Based on the morphological feature, the arrangement for the input is carefully divided so that 70% for training 15% for validation, and 15% for testing sets. Artificial Neural Network (ANN) was used to classify the plaque psoriasis skin disease severity stages as mild, moderate, and severe. The performance measure of the classifier accuracy was computed. It was observed that the proposed scheme with ANN classifier outperformed by giving 90% accuracy and a 10% probability of misclassification error to classify plaque psoriasis as mild, moderate, or severe. All the techniques were implemented through MATLAB R2018a platform and the design and implementation of these image processing techniques help easy detection of plaque psoriasis skin diseases.