ArticleJournal of digital imaging2020
Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet.
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 37 papers, 3 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.Journal of imaging informatics in medicine · 2025Article
- Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Skin cancer detection using dermoscopic images with convolutional neural network.Scientific reports · 2025Article
- Advances in computer vision and deep learning-facilitated early detection of melanoma.Briefings in functional genomics · 2025Review
- Enhancing Deep Learning Model Explainability in Brain Tumor Datasets Using Post-Heuristic Approaches.Journal of imaging · 2024Article
- Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm.Visual computing for industry, biomedicine, and art · 2024Article
- Review
- An approach to the dermatological classification of histopathological skin images using a hybridized CNN-DenseNet model.PeerJ. Computer science · 2024Article
- Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Sickle cell disease classification using deep learning.Heliyon · 2023Article
- White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning.Scientific reports · 2023Article
- Article
- Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System.Healthcare (Basel, Switzerland) · 2023Article
- Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions.Diagnostics (Basel, Switzerland) · 2023Article
- A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions with Swish Activations.Journal of healthcare engineering · 2023Article
- A Review Paper about Deep Learning for Medical Image Analysis.Computational and mathematical methods in medicine · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtained results, we could say that the proposed method achieved a great success where it accurately classifies the skin lesions into seven classes. These classes are melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The achieved percentages are 98.70%, 95.60%, 99.27%, and 95.06% for accuracy, sensitivity, specificity, and precision, respectively.
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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.