ArticleSensors (Basel, Switzerland)2022
SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep Learning Algorithms in the Diagnosis of Basal Cell Carcinoma Using Dermatoscopy: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.Sensors (Basel, Switzerland) · 2023Pooled it
- Assessment of high- and low-risk histopathologic subtypes of basal cell carcinoma using artificial intelligence and dermatologist evaluation.JAAD international · 2026Article
- Article
- Skin cancer detection using late fusion of pretrained models.Scientific reports · 2026Article
- DermaScanAI an explainable hybrid deep learning framework for automated skin lesion classification using dual attention and metadata fusion.Scientific reports · 2026Article
- Automated skin cancer detection using MedFusionNet with attention-based fusion of ConvNeXt and vision transformer.Scientific reports · 2025Article
- Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.Scientific reports · 2025Article
- Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.Journal of imaging informatics in medicine · 2025Article
- A robust deep learning framework for multiclass skin cancer classification.Scientific reports · 2025Article
- Towards unbiased skin cancer classification using deep feature fusion.BMC medical informatics and decision making · 2025Article
- Minimal sourced and lightweight federated transfer learning models for skin cancer detection.Scientific reports · 2025Article
- Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios.Scientific reports · 2025Article
- Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic Accuracy of Dermoscopic Characteristics in Cutaneous Squamous Cell Carcinoma: A Retrospective Analysis.Journal of skin cancer · 2025Article
- Next-generation approach to skin disorder prediction employing hybrid deep transfer learning.Frontiers in big data · 2025Article
- AutoTFCNNY: A multi-instance neural network for enhanced early cancer detection using TCR data.PloS one · 2025Article
- Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens.Scientific reports · 2024Review
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.
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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.