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All Journal Infact: Jurnal Sains dan Komputer
Tyara Regina Nadya Putri
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Computer Science & IT Electrical & Electronics Engineering

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Enhanced Dermatological Diagnosis: Autoimmune and Non-Autoimmune Skin Disease Classification Using MobileNet and ResNet Tyara Regina Nadya Putri; Widodo, Agung Mulyo
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.711

Abstract

Autoimmune diseases arise when the immune system mistakenly attacks the body's healthy cells, causing a range of symptoms that can greatly affect a patient's quality of life. In Indonesia, these conditions present a significant public health concern. According to research by Ministry of Health Republic Indonesia in 2024, autoimmune lupus affects approximately 0.5% of the population, impacting over 1.3 million individuals. This study proposes a classification and detection model utilizing Convolutional Neural Networks (CNN) with transfer learning, incorporating MobileNetV2, MobileNetV3Small, MobileNetV3Large, ResNet50, ResNet101, and ResNet152 architectures. The model's performance is assessed using a confusion matrix, evaluating precision, recall, and F1-score, while computational efficiency is analyzed using a GPU T4. Experimental results demonstrate that ResNet152 achieved the highest accuracy at 92%. These findings emphasize the crucial role of selecting an optimal CNN architecture to enhance the accuracy of autoimmune and non-autoimmune skin disease classification and detection.
Co-Authors Widodo, Agung Mulyo