The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
Open accessAbstract
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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ImageNet classification with deep convolutional neural networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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You Only Look Once: Unified, Real-Time Object Detection
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Cited by
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
Terven, J., Córdova-Esparza, D.-M., & Romero-González, J.-A. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5040083 · Cited by 3,052
YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO
Hussain, M. (2024) · IEEE Access · doi:10.1109/access.2024.3378568 · Cited by 492
Sapkota, R., Flores-Calero, M., Qureshi, R., Badgujar, C., Nepal, U., Poulose, A., Zeno, P., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 308
Skin disease diagnosis with deep learning: a review
Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178
Primiero, C. A., Rezze, G. G., Caffery, L. J., Carrera, C., Podlipnik, S., Espinosa, N., Puig, S., Janda, M., Soyer, H. P., & Malvehy, J. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.11.007 · Cited by 30
Metadata
- Authors
- Ünver, H. M., & Ayan, E.
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2019-07-10
- Volume
- 9
- Issue
- 3
- Pages
- 72
- ISSN
- 2075-4418 (ISSN-L)
- OpenAlex
- W2960571991
- Cited by
- 302
- References
- 18
- FWCI
- 12.06
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ünver, H. M., & Ayan, E. (2019). Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm. Diagnostics. https://doi.org/10.3390/diagnostics9030072
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