Aga Maulana, Teuku R. Noviandy, Rivansyah Suhendra, Nanda Earlia, Cita R. S. Prakoeswa, Tara S. Kairupan, Ghifari M. Idroes, Muhammad Subianto, Rinaldi Idroes
Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen’s Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2–93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required. © 2024, Narra Sains Indonesia. All rights reserved.
Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Information Technology, Faculty of Science and Technology, Universitas Islam Negeri Ar-Raniry, Banda Aceh, Indonesia; Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Meulaboh, Indonesia; Dermatology Division, Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia; Department of Dermatology and Venereology, Faculty of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Dermatology and Venereology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia; Faculty of Medicine, Universitas Sam Ratulangi, Manado, Indonesia; Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta, Indonesia; Department of Statistic, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
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