ArticleJournal of healthcare engineering2022
Deep Learning Application for Effective Classification of Different Types of Psoriasis.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- A review of psoriasis image analysis based on machine learning.Frontiers in medicine · 2024Pooled it
- Combatting psoriasis with nanomedicine: gamma-amino butyric acid-chitosan nanoparticles as a targeted anti-psoriatic therapy.Inflammopharmacology · 2026Article
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Advancements in psoriasis classification using custom transfer learning algorithms.Scientific reports · 2026Article
- Psoriasis: A Multidimensional Review of Onset, Progression, Treatment, and the Evolution of Disease Models.Molecular diagnosis & therapy · 2025Review
- Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.Scientific reports · 2025Article
- A Hybrid Model for Psoriasis Subtype Classification: Integrating Multi Transfer Learning and Hard Voting Ensemble Models.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis.Scientific reports · 2024Article
- Review
- Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Diagnosing Melanomas in Dermoscopy Images Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Based Psoriasis Severity Assessment: Real-world Study and Application.Journal of medical Internet research · 2023Article
- Nanoscale Topical Pharmacotherapy in Management of Psoriasis: Contemporary Research and Scope.Journal of functional biomaterials · 2022Review
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have demonstrated success in the prediction of skin diseases and can also lead to the classification of different types of psoriasis. Hence, we propose a deep learning-based application for effective classification of five types of psoriasis namely, plaque, guttate, inverse, pustular, and erythrodermic as well as the prediction of normal skin. We used 172 images of normal skin from the BFL NTU dataset and 301 images of psoriasis from the Dermnet dataset. The input sample images underwent image preprocessing including data augmentation, enhancement, and segmentation which was followed by color, texture, and shape feature extraction. Two deep learning algorithms of convolutional neural network (CNN) and long short-term memory (LSTM) were applied with the classification models being trained with 80% of the images. The reported accuracies of CNN and LSTM are 84.2% and 72.3%, respectively. A paired sample
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.