ArticleScientific reports2025
A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- DVM-SLC: A Dual-View Meta-Aware Model for Reliable Multi-Class Skin Lesion Classification from Clinical and Dermoscopic Images.Diagnostics (Basel, Switzerland) · 2026Article
- Beyond Accuracy: A MultiDimensional Framework for Evaluating Medical Image Classification Through Win vs. Lose Model Comparisons.Journal of imaging informatics in medicine · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- A robust stacked ensemble strategy with multi-optimizer CNN models for skin cancer classification.Scientific reports · 2026Article
- CBAM-Xception: An Attention-Guided Framework for Skin Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Manifold topological deep learning for biomedical data.Nature communications · 2026Article
- Explainable ensemble transfer learning for skin lesion classification with multi-method explainability validation.Frontiers in public health · 2026Article
- An optimal graph convolutional vision neural network with explainable feature optimization for improved skin cancer detection.BMC medical imaging · 2025Article
- An Explainable Fuzzy Framework for Assessing Preeclampsia Classification.Biomedicines · 2025Article
- AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.Journal of nanobiotechnology · 2025Article
- Explainable AI-driven hybrid deep learning framework for accurate skin cancer diagnosis.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Skin cancer is widespread and can be potentially fatal. According to the World Health Organisation (WHO), it has been identified as a leading cause of mortality. It is essential to detect skin cancer early so that effective treatment can be provided at an initial stage. In this study, the widely-used HAM10000 dataset, containing high-resolution images of various skin lesions, is employed to train and evaluate. Our methodology for the HAM10000 dataset involves balancing the imbalanced dataset by augmenting images followed by splitting the dataset into train, test and validation set, preprocessing the images, training the individual models Xception, InceptionResNetV2 and MobileNetV2, and then combining their outputs using fuzzy logic to generate a final prediction. We examined the performance of the ensemble using standard metrics like classification accuracy, confusion matrix, etc. and achieved an impressive accuracy of 95.14% and the result demonstrates the effectiveness of our approach in accurately identifying skin cancer lesions. To further assess the efficiency of the model, additional tests have been performed on the DermaMNIST dataset from the MedMNISTv2 collection. The model performs well on the dataset and transcends the benchmark accuracy of 76.8%, achieving 78.25%. Thus the model is efficient for skin cancer classification, showcasing its potential for clinical applications.
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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 Socratic graph.