Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Tue, Apr 28, 2026
[Archive]
Volume 40, Issue 1 (1-2026)
Med J Islam Repub Iran 2026 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Masoorian H, Gholamzadeh M, Firooz A, Safdari R. Image Processing for Diagnosing Psoriasis: A Machine Learning Approach to Classify Skin Lesions into Psoriasis Subtypes. Med J Islam Repub Iran 2026; 40 (1) :114-122
URL: http://mjiri.iums.ac.ir/article-1-9741-en.html
URL: http://mjiri.iums.ac.ir/article-1-9741-en.html
Health Information Management and Medical informatics Department, School of Allied Medical Science, Tehran University of Medical Sciences, Tehran, Iran , rsafdari@tums.ac.ir
Abstract: (24 Views)
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.
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.
Type of Study: Original Research |
Subject:
Dermatology
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
