ArticleMultimedia tools and applications2023
Classification of skin cancer from dermoscopic images using deep neural network architectures.
Article in Multimedia tools and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- Automated Melanocytic Lesion Classification: Capsule Networks Trained With Synthetic Images Can Outperform Networks Trained With Real Images.The Australasian journal of dermatology · 2026Article
- From local textures to global attention: a hybrid feature fusion network for skin lesion classification.Scientific reports · 2026Article
- Skin cancer detection using late fusion of pretrained models.Scientific reports · 2026Article
- Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings.Bioengineering (Basel, Switzerland) · 2026Article
- Hybrid deep feature fusion and ensemble learning for multi-class skin lesion classification.Frontiers in artificial intelligence · 2026Article
- Explainable ensemble transfer learning for skin lesion classification with multi-method explainability validation.Frontiers in public health · 2026Article
- Automated skin cancer detection using MedFusionNet with attention-based fusion of ConvNeXt and vision transformer.Scientific reports · 2025Article
- An optimal graph convolutional vision neural network with explainable feature optimization for improved skin cancer detection.BMC medical imaging · 2025Article
- Effective skin cancer classification by modified and optimized inception-ResNet-V2 model.Scientific reports · 2025Article
- Optimized auxiliary classifier Wasserstein generative adversarial network fostered skin cancer classification from dermoscopic images.Scientific reports · 2025Article
- An Improved Capsule Network for Image Classification Using Multi-Scale Feature Extraction.Journal of imaging · 2025Article
- Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.Scientific reports · 2025Article
- Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.Journal of imaging informatics in medicine · 2025Article
- CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Next-generation approach to skin disorder prediction employing hybrid deep transfer learning.Frontiers in big data · 2025Article
- Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens.Scientific reports · 2024Review
- The potential benefit of artificial intelligence regarding clinical decision-making in the treatment of wrist trauma patients.Journal of orthopaedic surgery and research · 2024Article
- Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making.Scientific reports · 2024Article
- Review
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4 authors.
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Abstract
A powerful medical decision support system for classifying skin lesions from dermoscopic images is an important tool to prognosis of skin cancer. In the recent years, Deep Convolutional Neural Network (DCNN) have made a significant advancement in detecting skin cancer types from dermoscopic images, in-spite of its fine grained variability in its appearance. The main objective of this research work is to develop a DCNN based model to automatically classify skin cancer types into melanoma and non-melanoma with high accuracy. The datasets used in this work were obtained from the popular challenges ISIC-2019 and ISIC-2020, which have different image resolutions and class imbalance problems. To address these two problems and to achieve high performance in classification we have used EfficientNet architecture based on transfer learning techniques, which learns more complex and fine grained patterns from lesion images by automatically scaling depth, width and resolution of the network. We have augmented our dataset to overcome the class imbalance problem and also used metadata information to improve the classification results. Further to improve the efficiency of the EfficientNet we have used ranger optimizer which considerably reduces the hyper parameter tuning, which is required to achieve state-of-the-art results. We have conducted several experiments using different transferring models and our results proved that EfficientNet variants outperformed in the skin lesion classification tasks when compared with other architectures. The performance of the proposed system was evaluated using Area under the ROC curve (AUC - ROC) and obtained the score of 0.9681 by optimal fine tuning of EfficientNet-B6 with ranger optimizer.
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