The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods
Open accessAbstract
Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth in the number of skin cancers, there is a growing need of computerised analysis for skin lesions. The state-of-the-art public available datasets for skin lesions are often accompanied with a very limited amount of segmentation ground truth labeling. Also, the available segmentation datasets consist of noisy expert annotations reflecting the fact that precise annotations to represent the boundary of skin lesions are laborious and expensive. The lesion boundary segmentation is vital to locate the lesion accurately in dermoscopic images and lesion diagnosis of different skin lesion types. In this work, we propose the fully automated deep learning ensemble methods to achieve high sensitivity and high specificity in lesion boundary segmentation. We trained the ensemble methods based on Mask R-CNN and DeeplabV3+ methods on ISIC-2017 segmentation training set and evaluate the performance of the ensemble networks on ISIC-2017 testing set and PH2 dataset. Our results showed that the proposed ensemble methods segmented the skin lesions with Sensitivity of 89.93% and Specificity of 97.94% for the ISIC-2017 testing set. The proposed ensemble method Ensemble-A outperformed FrCN, FCNs, U-Net, and SegNet in Sensitivity by 4.4%, 8.8%, 22.7%, and 9.8% respectively. Furthermore, the proposed ensemble method Ensemble-S achieved a specificity score of 97.98% for clinically benign cases, 97.30% for the melanoma cases, and 98.58% for the seborrhoeic keratosis cases on ISIC-2017 testing set, exhibiting better performance than FrCN, FCNs, U-Net, and SegNet.
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Microsoft COCO: Common Objects in Context
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Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
The pascal visual object classes (VOC) challenge
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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
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Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
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Cited by
Analysis of the ISIC image datasets: Usage, benchmarks and recommendations
Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., & Yap, M. H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102305 · Cited by 307
Self-citationDeep Learning-Based Transfer Learning for Classification of Skin Cancer
Jain, S., Singhania, U., Tripathy, B., Nasr, E. A., Aboudaif, M. K., & Kamrani, A. K. (2021) · Sensors · doi:10.3390/s21238142 · Cited by 169
Deep learning in dermatology: a systematic review of current approaches, outcomes, and limitations
Jeong, H. K., Park, C., Henao, R., & Kheterpal, M. (2023) · JID Innovations · doi:10.1016/j.xjidi.2022.100150 · Cited by 134
Early Melanoma Detection Based on a Hybrid YOLOv5 and ResNet Technique
Elshahawy, M., Elnemr, A., Oproescu, M., Schiopu, A.-G., Elgarayhi, A., Elmogy, M. M., & Sallah, M. (2023) · doi:10.20944/preprints202307.1970.v1 · Cited by 51
Boosting skin cancer diagnosis accuracy with ensemble approach
Natha, P., Tera, S. P., Chinthaginjala, R., Rab, S. O., Narasimhulu, C. V., & Kim, T. H. (2025) · Scientific Reports · doi:10.1038/s41598-024-84864-5 · Cited by 45
Metadata
- Authors
- Goyal, M., Oakley, A., Bansal, P., Dancey, D., & Yap, M. H.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 4171-4181
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2996717109
- Cited by
- 313
- References
- 31
- FWCI
- 13.42
- Type
- journal article
- Language
- en
- Open access
- gold
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