Developing Skin Diseases Diagnosis Model Using Convolutional Neural Network.

Abstract

Skin diseases are the most commonly recognized health issues worldwide and are associated with an extensive global burden; therefore, an easy and efficient diagnosis of skin diseases would be crucial to ensuring the appropriate and effective treatment for the patient. Although there are many computerized methods for diagnosing skin diseases, the convolutional neural network has proven superior to the conventional methods.in recent research remarkable accuracy is achieved but accuracy should be improved. This paper classifies ten types of skin diseases: atopic Dermatitis, acne vulgaris, basal cell carcinoma, healthy lichen planus, melanocytic nevi, melanoma, onychomycosis, tinea capitis, and vascular tumor. The primary objective of this paper is to develop a skin disease diagnosis model by comparing different CNN architectures and evaluating the performance of these deep learning networks on skin lesion images. The dataset was collected from the Goba referral hospital and labeled by professionals. the lesion classification is implemented through transfer learning on four CNN architectures: ResNet50, DenseNet201, MobileNetV2, and MobileNetV3. The dataset used for these experiments is a custom dataset of about 21,000 images. The results show that DenseNet 201 and Mobile Net v3 perform best with 89.2% and 89.0% accuracy, respectively. The proposed work shows the various parameters, including the accuracy of all four deep learning networks, which helped build an efficient automated classification model for multiple skin Diseases