ArticleHealthcare (Basel, Switzerland)2022
Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning.
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 65 papers.
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Who cites it
65 citing papers in PubMed.
- Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics.Health information science and systems · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- SAVE: Spectrum-Aided Visual Enhancement for AI-Based Skin Cancer Detection.Diagnostics (Basel, Switzerland) · 2026Article
- TriDermCancerNet: A hybrid deep learning framework for skin cancer classification.The Journal of international medical research · 2026Article
- Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.Journal of advanced research · 2026Article
- From local textures to global attention: a hybrid feature fusion network for skin lesion classification.Scientific reports · 2026Article
- Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings.Bioengineering (Basel, Switzerland) · 2026Article
- Modelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2.Scientific reports · 2026Article
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- Analysis of hybrid CNN models optimized with metaheuristic algorithms for melanoma detection.Scientific reports · 2026Article
- Incremental learning approach for semantic segmentation of skin histology images.Scientific reports · 2026Article
- Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model.Biomedicines · 2026Article
- Integrating features of radiomics and CNN models for early skin cancer detection based on watershed segmentation.Discover oncology · 2026Article
- Foundation-Model-Driven Skin Lesion Segmentation and Classification Using SAM-Adapters and Vision Transformers.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable depth-wise and channel-wise fusion models for multi-class skin lesion classification.PloS one · 2026Article
- Multimodal skin lesion classification for early cancer diagnosis using deep learning.Frontiers in physiology · 2026Article
- Multimodal deep learning ensemble framework for skin cancer detection.Scientific reports · 2025Article
- Modified EfficientNet-B0 Architecture Optimized with Quantum-Behaved Algorithm for Skin Cancer Lesion Assessment.Diagnostics (Basel, Switzerland) · 2025Article
- Privacy preserving skin cancer diagnosis through federated deep learning and explainable AI.Scientific reports · 2025Article
- Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care.International journal of emergency medicine · 2025Review
5 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study's innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.
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