ArticleDiagnostics (Basel, Switzerland)2023
Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 50 citations in OpenAlex.
- Skin cancer detection using late fusion of pretrained models.Scientific reports · 2026Article
- Article
- Automated skin cancer detection using MedFusionNet with attention-based fusion of ConvNeXt and vision transformer.Scientific reports · 2025Article
- Semi-supervised GAN with hybrid regularization and evolutionary hyperparameter tuning for accurate melanoma detection.Scientific reports · 2025Article
- Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.Scientific reports · 2025Article
- Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.Scientific reports · 2025Article
- Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.Scientific reports · 2025Article
- Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP.Journal of imaging informatics in medicine · 2024Article
- A novel skin cancer detection model using modified finch deep CNN classifier model.Scientific reports · 2024Article
- Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks.International journal of molecular sciences · 2024Article
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio.
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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.