ArticleBiomolecules2020
The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 4 of them syntheses that pooled it.
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Who cites it
70 citing papers in PubMed, 4 syntheses or guidelines pooled it, 252 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.BMC primary care · 2025Pooled it
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review.Dermatology (Basel, Switzerland) · 2023Pooled it
- Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features.Computational and mathematical methods in medicine · 2022Trial
- Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model.Biomedicines · 2026Article
- SIG-CFFNet: Structural Information-Guided Cascaded Feature Fusion Network for Gastrointestinal Anatomy Classification.Annals of biomedical engineering · 2026Article
- Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images.Biomedicines · 2026Review
- Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis.Frontiers in oncology · 2026Review
- Multi-task AI models in dermatology: Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Deep learning model for diagnosing lupus erythematosus in cardiac patients using ECG and audio spectrograms.Scientific reports · 2025Article
- Artificial intelligence in disease diagnostics: a comprehensive narrative review of current advances, applications, and future challenges in healthcare.Annals of medicine and surgery (2012) · 2025Review
- 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
- Deep Learning-Based Classification System for Facial Pigmented Lesions to Aid Laser Treatment Decisions.Cureus · 2025Article
- The Use of Artificial Intelligence for Skin Cancer Detection in Asia-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- Establishment of a machine learning model for predicting splenic hilar lymph node metastasis.NPJ digital medicine · 2025Article
- Current status and future direction of cancer research using artificial intelligence for clinical application.Cancer science · 2025Review
- Clinical Application of Artificial Intelligence in Ultrasound Imaging for Oncology.JMA journal · 2025Review
- Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms.Diagnostics (Basel, Switzerland) · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (
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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.