ArticleJournal of digital imaging2020
Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the ABCD Rule.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- HCHS-Net: A Multimodal Handcrafted Feature and Metadata Framework for Interpretable Skin Lesion Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Skin tumor identification by means of convolutional neural network and improved gray wolf optimizer.Frontiers in oncology · 2026Article
- Effective skin cancer classification by modified and optimized inception-ResNet-V2 model.Scientific reports · 2025Article
- Towards unbiased skin cancer classification using deep feature fusion.BMC medical informatics and decision making · 2025Article
- Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification.PeerJ. Computer science · 2025Article
- Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability.BioData mining · 2024Article
- An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks.Diagnostics (Basel, Switzerland) · 2024Article
- Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- DANNET: deep attention neural network for efficient ear identification in biometrics.PeerJ. Computer science · 2024Article
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
- Article
- Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images.Journal of digital imaging · 2023Article
- Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring.Diagnostics (Basel, Switzerland) · 2023Article
- Melanoma skin cancer detection using mask-RCNN with modified GRU model.Frontiers in physiology · 2023Article
- Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning.Cancers · 2022Article
- SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.Sensors (Basel, Switzerland) · 2022Article
- An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer.Sensors (Basel, Switzerland) · 2022Article
- Review
- A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification.Journal of healthcare engineering · 2022Article
- The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas.Journal of skin cancer · 2022Article
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4 authors.
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Abstract
According to statistics of the American Cancer Society, in 2015, there are about 91,270 American adults diagnosed with melanoma of the skin. For the European Union, there are over 90,000 new cases of melanoma annually. Although melanoma only accounts for about 1% of all skin cancers, it causes most of the skin cancer deaths. Melanoma is considered one of the fastest-growing forms of skin cancer, and hence the early detection is crucial, as early detection is helpful and can provide strong recommendations for specific and suitable treatment regimens. In this work, we propose a method to detect melanoma skin cancer with automatic image processing techniques. Our method includes three stages: pre-process images of skin lesions by adaptive principal curvature, segment skin lesions by the colour normalisation and extract features by the ABCD rule. We provide experimental results of the proposed method on the publicly available International Skin Imaging Collaboration (ISIC) skin lesions dataset. The acquired results on melanoma skin cancer detection indicates that the proposed method has high accuracy, and overall, a good performance: for the segmentation stage, the accuracy, Dice, Jaccard scores are 96.6%, 93.9% and 88.7%, respectively; and for the melanoma detection stage, the accuracy is up to 100% for a selected subset of the ISIC dataset.
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