ReviewDiagnostics (Basel, Switzerland)2021
Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence with deep learning driven entropy-curvature attention mechanism for detection and segmentation of skin lesions using biomedical images.Scientific reports · 2026Article
- Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.Journal of advanced research · 2026Article
- Modelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2.Scientific reports · 2026Article
- Multimodal models for skin cancer classification using clinical freetext and dermatoscopic images.Communications medicine · 2026Article
- Improving Normal/Abnormal and Benign/Malignant Classifications in Mammography with ROI-Stratified Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Mask of Truth: Model Sensitivity to Unexpected Regions of Medical Images.Journal of imaging informatics in medicine · 2026Article
- Visual and lesion descriptor fusion using cross-attention for skin disease diagnosis.Frontiers in oncology · 2026Article
- 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
- Deep learning algorithms for identifying developmental dysplasia of the hip based on sonographic images: a retrospective, prospective, multicenter study in China.EClinicalMedicine · 2025Article
- Prediction and Classification the Risk of Stroke Patients Using Machine Learning Techniques: A Retrospective Cross-Sectional Study.Health science reports · 2025Article
- Article
- A probabilistic detection-based approach to skin and freckle segmentation.Scientific reports · 2025Article
- Demographic, Morphological, and Histopathological Characteristics of Melanoma and Nevi: Insights from Statistical Analysis and Machine Learning Models.Diagnostics (Basel, Switzerland) · 2025Article
- Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.
Indexed as
Identifiers
What OpenQuestion holds
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.