ArticleFrontiers in medicine2024
LesionNet: an automated approach for skin lesion classification using SIFT features with customized convolutional neural network.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Transformer-Based Foundation Learning for Robust and Data-Efficient Skin Disease Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- H-fusion SEG: dual-branch hyper-attention fusion network with SAM integration for robust skin disease segmentation.Scientific reports · 2025Article
- MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation.Frontiers in medicine · 2025Article
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Authors and funding
8 authors.
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Abstract
Accurate detection of skin lesions through computer-aided diagnosis has emerged as a critical advancement in dermatology, addressing the inefficiencies and errors inherent in manual visual analysis. Despite the promise of automated diagnostic approaches, challenges such as image size variability, hair artifacts, color inconsistencies, ruler markers, low contrast, lesion dimension differences, and gel bubbles must be overcome. Researchers have made significant strides in binary classification problems, particularly in distinguishing melanocytic lesions from normal skin conditions. Leveraging the "MNIST HAM10000" dataset from the International Skin Image Collaboration, this study integrates Scale-Invariant Feature Transform (SIFT) features with a custom convolutional neural network model called LesionNet. The experimental results reveal the model's robustness, achieving an impressive accuracy of 99.28%. This high accuracy underscores the effectiveness of combining feature extraction techniques with advanced neural network models in enhancing the precision of skin lesion detection.
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