A Novel Light-Weight Approach for the Classification of Different Types of Psoriasis Disease Using Depth Wise Separable Convolution Neural Networks

Abstract

A Novel Light-Weight Approach for the Classification of Different Types of Psoriasis Disease Using Depth Wise Separable Convolution Neural Networks T R Arunkumar1∗, H S Jayanna2 1 Research Scholar, Department of Computer Science & Engineering, Siddaganga Institute of Technology, Tumkur, India 2 Professor and Head, Department of Information Science, Siddaganga Institute of Technology, Tumkur, India Abstract Objectives: The main objective of the work is to classify the psoriasis affected skin into one of different psoriasis types viz. erythrodermic, gutatte, inverse, nail, plaque, and pustular using depth wise separable convolution neural networks. Methods: To identify the type of psoriasis disorder, the experiment uses MobileNet machine learning architecture which is based on depth- wise separable convolutions. In the preprocessing step, the input images are segmented into 224 pixels X 224 pixels with the help of the keras KerasImageDataGenerator function and in next step, these segmented images are fed as input to 28 layers of reconstructed MobileNet architecture. A series of convolutions and depth-wise separable convolutions layers are applied on the input images. The rectified linear activation function is applied to introduce non-linearity in the network. Adam optimizer algorithm is used for training the network. Categorical cross-entropy is used for the comparison of the accuracy of the experimental results with the existing work of classification of psoriasis disorder. Findings: Using MobileNet machine learning architecture, the experiments attained around 86% of classification efficiency, and an to the work done in (1). Novelty: The novelty of the work lies in the prediction average F1− score of .94 as compared with VGG19 and ResNet-34, compared of the types of the psoriasis disorder accurately with less turnaround time with an accuracy of 86% consuming low processing capability which can be implemented on low powered hand-held devices. Keywords: Machine Learning; Depth Wise Separable Convolution; Psoriasis Disorder; ReLU Activation; Pointwise convolutions