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.
