ArticlePloS one2024
DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Revolutionizing dermatopathology using AI in skin diagnostics: scoping review.Frontiers in medicine · 2026Pooled it
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- An attention based optimized network for the classification of skin lesions.Scientific reports · 2026Article
- An optimal graph convolutional vision neural network with explainable feature optimization for improved skin cancer detection.BMC medical imaging · 2025Article
- Optimizing breast cancer classification based on cat swarm-enhanced ensemble neural network approach for improved diagnosis and treatment decisions.Scientific reports · 2025Article
- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- A robust deep learning framework for multiclass skin cancer classification.Scientific reports · 2025Article
- Towards unbiased skin cancer classification using deep feature fusion.BMC medical informatics and decision making · 2025Article
- Minimal sourced and lightweight federated transfer learning models for skin cancer detection.Scientific reports · 2025Article
- Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios.Scientific reports · 2025Article
- AutoTFCNNY: A multi-instance neural network for enhanced early cancer detection using TCR data.PloS one · 2025Article
- Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems.PloS one · 2025Article
- Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens.Scientific reports · 2024Review
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
- 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
- CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis.Scientific reports · 2024Article
- Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.Scientific reports · 2024Article
- Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model's accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.
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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.