ArticleDiagnostics (Basel, Switzerland)2019
Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm.
Article in Diagnostics (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Pooled it
- New Trends in Melanoma Detection Using Neural Networks: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- SkinFormer: a hybrid vision transformer and ConvNeXtV2 approach for skin cancer detection and segmentation.Scientific reports · 2026Article
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- DL-PCMNet: Distributed Learning Enabled Parallel Convolutional Memory Network for Skin Cancer Classification with Dermatoscopic Images.Diagnostics (Basel, Switzerland) · 2026Article
- Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment.Biomedical engineering and computational biology · 2026Article
- A stomata imaging and segmentation pipeline incorporating generative AI to reduce dependency on manual groundtruthing.Plant methods · 2025Article
- An Automated Diagnosis of Myopia from an Optic Disc Image Using YOLOv11: A Feasible Approach for Non-Expert ECPs in Computer Vision.Life (Basel, Switzerland) · 2025Article
- Deep Learning-Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization.Journal of medical Internet research · 2025Article
- Article
- Skin Lesion Classification Through Test Time Augmentation and Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Improved Skin Lesion Segmentation in Dermoscopic Images Using Object Detection and Semantic Segmentation.Clinical, cosmetic and investigational dermatology · 2025Article
- SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis.Health care science · 2024Article
- Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.Archives of dermatological research · 2024Article
- Article
- [Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Article
- An efficient multi-class classification of skin cancer using optimized vision transformer.Medical & biological engineering & computing · 2024Article
- Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.NPJ precision oncology · 2024Article
- Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach.BMC bioinformatics · 2023Article
- An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal.Sensors (Basel, Switzerland) · 2023Article
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2 authors.
Funding
Abstract
Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.
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