ArticleCancers2023
SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 50 citations in OpenAlex.
- An Attention-Enhanced Multimodal Hybrid Model for Skin Cancer Diagnosis Using Imaging and Clinical Data.Biomedicines · 2026Article
- ZACP: enhancing skin lesion classification ability using zero attention and complete perception.Visual computing for industry, biomedicine, and art · 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
- HCHS-Net: A Multimodal Handcrafted Feature and Metadata Framework for Interpretable Skin Lesion Classification.Biomimetics (Basel, Switzerland) · 2026Article
- A hybrid deep learning and cellular automata framework with fractional derivatives for skin type and skin disease classification.Frontiers in digital health · 2026Article
- AI Advancements in Healthcare: Medical Imaging and Sensing Technologies.Bioengineering (Basel, Switzerland) · 2025Article
- SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems.Scientific reports · 2025Article
- CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data.Cancers · 2025Article
- MLG: a mixed local and global model for brain tumor classification.Frontiers in neuroscience · 2025Article
- Enhancing dermatological diagnosis for differentiating actinic from seborrheic keratosis using deep learning model.Frontiers in medicine · 2025Article
- Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems.PloS one · 2025Article
- MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.Journal of imaging · 2024Article
- A novel deep semantic- and vision-based self-attention architecture for skin cancer classification.Digital healthArticle
- Dual concatenated transfer learning with attention fusion: An ensemble-enhanced approach for skin lesion classification.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet's performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet's effectiveness, as it consistently outperformed both benchmarks across all datasets.
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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.