Classification of Atopic Dermatitis and Psoriasis Skin Diseases Using Residual Network (ResNet-50)

Rakhimatulfitria Mekacahyani; Badie’ah Badie’ah; Imam Much Ibnu Subroto

doi:10.30812/bite.v6i2.4164

Rakhimatulfitria Mekacahyani Universitas Islam Sultan Agung, Semarang, Indonesia
Badie’ah Badie’ah Universitas Islam Sultan Agung, Semarang, Indonesia
Imam Much Ibnu Subroto Universitas Islam Sultan Agung, Semarang, Indonesia

DOI: https://doi.org/10.30812/bite.v6i2.4164

Abstract

Background: Atopic dermatitis and psoriasis are common skin diseases with similar symptoms, characterized by abnormally red or inflamed epidermal lesions and varying degrees of skin thickening. However, they are distinct conditions, making it crucial to understand how to differentiate between them. This understanding can help reduce stigma and the risk of comorbidities, thereby improving patients' quality of life and preventing more serious health risks.

Objective: The aim of this research is to increase accuracy in classifying the skin diseases atopic dermatitis and psoriasis using the Residual Network (ResNet-50) model without overfitting, and compare it with the MobileNet model to find the best approach.

Method: The method used in this study is the ResNet-50 architecture for skin disease classification, namely atopic dermatitis and psoriasis. The selection of the ResNet-50 model is based on the use of shortcut connections that allow the application of deeper networks without experiencing the problem of vanishing gradients.

Result: The results showed that the best accuracy reached 92.75% for training data and 88.00% for testing data, with a data ratio of 80%:10%:10%. In addition, the confusion matrix results from the best model showed that the precision, recall, and F1 score values for both diseases were between ≥80% and ≤96%.

Conclusion: The ResNet-50 method in scenario 1 outperformed other scenarios, improving classification accuracy and enhancing diagnostic effectiveness and medical practice development.

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