ArticleSensors (Basel, Switzerland)2023
Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review.BMC medical informatics and decision making · 2025Pooled it
- A review of psoriasis image analysis based on machine learning.Frontiers in medicine · 2024Pooled it
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Image Processing for Diagnosing Psoriasis: A Machine Learning Approach to Classify Skin Lesions into Psoriasis Subtypes.Medical journal of the Islamic Republic of Iran · 2026Article
- Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema.Scientific reports · 2025Article
- Diagnosis of psoriasis and lichen planus in real-time using neural networks based on skin Biomechanical properties obtained from numerical simulation.Scientific reports · 2025Article
- MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.Journal of imaging informatics in medicine · 2025Article
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- Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach.Scientific reports · 2024Article
- Single-cell analysis and machine learning identify psoriasis-associated CD8Frontiers in genetics · 2024Article
- A Mobile Health App for Facilitating Disease Management in Children With Atopic Dermatitis: Feasibility and Impact Study.JMIR dermatology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals' quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated "Derma Care" using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.
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Registered trials
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.