Medical Journal of the Islamic Republic Of Iran

en Image Processing for Diagnosing Psoriasis: A Machine Learning Approach to Classify Skin Lesions into Psoriasis Subtypes Dermatology Dermatology Original Research Original Research     Background: Psoriasis is a chronic autoimmune skin condition that affects 2-3% of the global population and manifests in various subtypes, including plaque, guttate, inverse, pustular, and erythrodermic psoriasis. Accurate subtype differentiation is crucial for effective treatment, but traditional diagnostic methods are time-consuming and prone to observer variability. This study aims to develop a machine learning model that classifies psoriasis lesions into the five primary subtypes using convolutional neural networks (CNNs) and transfer learning, offering a scalable tool to assist clinicians in diagnosing psoriasis and making informed treatment decisions.    Methods: This is a methodological–developmental study that develops and evaluates a deep learning model for psoriasis subtype classification. The dataset was obtained from from Kaggle, applying image augmentation techniques (rotation, translation, shearing, flipping, zoom) to enhance dataset diversity. A pre-trained Visual Geometry Group 16-layer architecture (VGG16) model was used for feature extraction, with a custom classification head added, incorporating ReLU-activated dense layers and dropout regularization to mitigate overfitting. The model was trained and evaluated using accuracy and loss metrics, with early stopping and model checkpointing for optimization.    Results: The model achieved 96% accuracy on the training dataset and 90% on the test dataset, demonstrating strong generalization. A confusion matrix analysis confirmed accurate differentiation between the five subtypes.    Conclusion: This study developed a deep learning model that accurately classifies psoriasis subtypes, utilizing CNNs and transfer learning. The model was integrated into a web-based tool, providing real-time diagnostic assistance for clinicians. This AI-driven system has the potential to enhance diagnostic accuracy, improve clinical workflows, and offer scalable solutions for psoriasis management, particularly in areas with limited access to dermatologists.   Psoriasis, Machine Learning, Convolutional Neural Networks (CNN), Image Augmentation 114 122 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-3027-2&slc_lang=en&sid=1 Hoorie Masoorian hmasoorian@gmail.com 200319475328460096614 200319475328460096614 No Health Information Management and Medical informatics Department, School of Allied Medical Science, Tehran University of Medical Sciences, Tehran, Iran Marsa Gholamzadeh marsa.gholamzadeh@yahoo.com 200319475328460096615 200319475328460096615 No Health Information Management and Medical informatics Department, School of Allied Medical Science, Tehran University of Medical Sciences, Tehran, Iran Alireza Firooz firozali@tums.ac.ir 200319475328460096616 200319475328460096616 No Center for Research and Training in Skin Diseases and Leprosy, Tehran University of Medical Sciences, Tehran, Iran Reza Safdari rsafdari@tums.ac.ir 200319475328460096617 200319475328460096617 Yes Health Information Management and Medical informatics Department, School of Allied Medical Science, Tehran University of Medical Sciences, Tehran, Iran