Open Access

ARTICLE

Early Skin Disease Identification Using eep Neural Network

by Vinay Gautam¹, Naresh Kumar Trivedi¹, Abhineet Anand¹, Rajeev Tiwari^2,*, Atef Zaguia³, Deepika Koundal⁴, Sachin Jain⁵

1 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
2 School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248007, Uttarakhand, India
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
4 Department of Systemics, University of Petroleum & Energy Studies, Dehradun, India
5 ABES Institute of Technology, Ghaziabad, India

* Corresponding Author: Rajeev Tiwari. Email:

Computer Systems Science and Engineering 2023, 44(3), 2259-2275. https://doi.org/10.32604/csse.2023.026358

Received 23 December 2021; Accepted 30 March 2022; Issue published 01 August 2022

Abstract

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy. Therefore, the primary goal of this paper is to classify, detect and provide accurate information about skin diseases. This paper deals with the same issue by proposing a high-performance Convolution neural network (CNN) to classify and detect skin disease at an earlier stage. The complete methodology is explained in different folds: firstly, the skin diseases images are pre-processed with processing techniques, and secondly, the important feature of the skin images are extracted. Thirdly, the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network (DCNN). The approach proposed in this paper is simple, fast, and shows accurate results up to 98% and used to detect six different disease types.

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Keywords

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