ReviewDiagnostics (Basel, Switzerland)2023
Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Interpretable Skin Cancer Identification Using a Hybrid Deep Learning and XAI Framework on HAM10000.Bioengineering (Basel, Switzerland) · 2026Article
- Text guided cross attentive multimodal learning with visual feature modulation for automated skin lesion detection.Scientific reports · 2026Article
- Synthetic skin image generation using a physics-based, object-to-image computational pipeline.International journal of computer assisted radiology and surgery · 2026Article
- Article
- Automatic identification of clinically importantEmerging microbes & infections · 2025Article
- Transformer-aided skin cancer classification using VGG19-based feature encoding.Scientific reports · 2025Article
- Article
- Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Ensemble Learning for Multiclass Skin Lesion Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Skin Lesions as Signs of Neuroenhancement in Sport.Brain sciences · 2025Review
- Enhancing skin lesion classification: a CNN approach with human baseline comparison.PeerJ. Computer science · 2025Article
- Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.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) · 2024Article
- MRI-Derived Dural Sac and Lumbar Vertebrae 3D Volumetry Has Potential for Detection of Marfan Syndrome.Diagnostics (Basel, Switzerland) · 2024Article
- Development, Application and Utility of a Machine Learning Approach for Melanoma and Non-Melanoma Lesion Classification Using Counting Box Fractal Dimension.Diagnostics (Basel, Switzerland) · 2024Article
- LesionNet: an automated approach for skin lesion classification using SIFT features with customized convolutional neural network.Frontiers in medicine · 2024Article
- Article
- Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.
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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.