Acne Vulgaris and Rosacea Skin Diseases Image Classification using Gray Level Co-Occurance Matrix and Convolutional Neural Network

Cahya Rahmad; Rosa Andrie Asmara; Alvionitha Sari Agstriningtyas

doi:10.1109/IEIT53149.2021.9587363

Acne Vulgaris and Rosacea Skin Diseases Image Classification using Gray Level Co-Occurance Matrix and Convolutional Neural Network

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Cahya Rahmad, Rosa Andrie Asmara, Alvionitha Sari Agstriningtyas

2021 Proceedings - IEIT 2021: 1st International Conference on Electrical and Information Technology Conference paper Cited by 8

Abstract

Teledermatology is one of the methods of dermatology treatment during the Covid-19 Pandemic which hit the world. Limiting physical contact between dermatologists and patients is a priority. This study aims to classify facial skin diseases with Rosacea and Acne Vulgaris. The disease is classified between Rosacea and three severity levels in Acne Vulgaris. The data used is taken from dermnet, kaggle and personal documentation. GLCM filters are used for feature extraction and are classified using Naïve Bayes and Convolutional Neural Network. Classification accuracy for GLCM is 45.30% and CNN Resnet-50 is 74,2424%. © 2021 IEEE.

Affiliations

Politeknik Negeri Malang, Department of Electrical Engineering, Malang, Indonesia; Politeknik Negeri Malang, Department of Information Technology, Malang, Indonesia

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