ArticleComputational and mathematical methods in medicine2018
Skin Disease Recognition Method Based on Image Color and Texture Features.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 142 citations in OpenAlex.
- Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- A review of psoriasis image analysis based on machine learning.Frontiers in medicine · 2024Pooled it
- Assessment of the Degree of Erythema Reduction in Rosacea After Polychromatic Light Treatments.Journal of clinical medicine · 2025Article
- A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases.Journal of imaging informatics in medicine · 2025Article
- PathoEye: A deep learning framework for the whole-slide image analysis of skin tissue.Computational and structural biotechnology journal · 2025Article
- Quantitative Image Analysis of Vascular Skin Response to Intense Pulsed Light Therapy.Clinical, cosmetic and investigational dermatology · 2025Article
- Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier.Diagnostics (Basel, Switzerland) · 2023Article
- Exploring the effect of automation failure on the human's trustworthiness in human-agent teamwork.Frontiers in robotics and AI · 2023Article
- An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease.Diagnostics (Basel, Switzerland) · 2022Article
- Skin color classification of Koreans using clustering.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2022Article
- Skin lesion classification system using a K-nearest neighbor algorithm.Visual computing for industry, biomedicine, and art · 2022Article
- Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge.Sensors (Basel, Switzerland) · 2022Article
- Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN).Computational intelligence and neuroscience · 2022Article
- Fuzzy segmentation and black widow-based optimal SVM for skin disease classification.Medical & biological engineering & computing · 2021Article
- Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin diseases have a serious impact on people's life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.
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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.