ArticleHealthcare (Basel, Switzerland)2022
Melanoma Detection Using Deep Learning-Based Classifications.
Article in Healthcare (Basel, Switzerland), 2022. 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.
- Differential Attention Feature Aggregator (DAFE) for Advanced Melanoma Detection.Journal of imaging informatics in medicine · 2026Article
- Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings.Bioengineering (Basel, Switzerland) · 2026Article
- Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model.Biomedicines · 2026Article
- Hybrid deep feature fusion and ensemble learning for multi-class skin lesion classification.Frontiers in artificial intelligence · 2026Article
- Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata.Frontiers in medicine · 2026Article
- An automated framework to classify skin lesions using Multi-Head Self Attention Layer-based Vision Transformers.Frontiers in artificial intelligence · 2026Article
- Improved CNN with BiLSTM model for early melanoma and skin lesion classification.Frontiers in artificial intelligence · 2026Article
- Advancing Medical Decision-Making with AI: A Comprehensive Exploration of the Evolution from Convolutional Neural Networks to Capsule Networks.Journal of imaging · 2025Review
- DSSCC net enhanced skin cancer classification using SMOTE Tomek and optimized convolutional neural network.Scientific reports · 2025Article
- Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
- Automatic melanoma detection using an optimized five-stream convolutional neural network.Scientific reports · 2025Article
- A hybrid fused-KNN based intelligent model to access melanoma disease risk using indoor positioning system.Scientific reports · 2025Article
- Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability.Scientific reports · 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
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
- AI in dermatology: a comprehensive review into skin cancer detection.PeerJ. Computer science · 2024Article
- The effect of hair removal and filtering on melanoma detection: a comparative deep learning study with AlexNet CNN.PeerJ. Computer science · 2024Article
- MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.Frontiers in cellular and infection microbiology · 2024Article
- Optimizing Skin Cancer Survival Prediction with Ensemble Techniques.Bioengineering (Basel, Switzerland) · 2023Article
- Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image's quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study's results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.
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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.