ArticleSensors (Basel, Switzerland)2022
Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Performance of SWE and Predictive Models Based on SWE for Post- Hepatectomy Liver Failure: A Systematic Review and Meta-analysis.Current medical imaging · 2025Pooled it
- Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.Sensors (Basel, Switzerland) · 2023Pooled it
- The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- XAI-MedNet: A Next-Generation Explainable AI Framework for Contrast-Enhanced Skin Lesion Classification via Entropy-Controlled Optimization.Bioengineering (Basel, Switzerland) · 2026Article
- An attention based optimized network for the classification of skin lesions.Scientific reports · 2026Article
- An automated framework to classify skin lesions using Multi-Head Self Attention Layer-based Vision Transformers.Frontiers in artificial intelligence · 2026Article
- A Multimodal Diagnostic Model for Hepatocellular Carcinoma Integrating Biomarkers Related to Programmed Cell Death and Radiomics Features.Current medicinal chemistry · 2026Article
- Review
- Article
- A robust deep learning framework for multiclass skin cancer classification.Scientific reports · 2025Article
- Skin disease diagnosis using decision and feature level fusion of deep features.Frontiers in digital health · 2025Article
- Using the power of artificial intelligence to improve the diagnosis and management of nonmelanoma skin cancer.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Review
- Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- [Skin lesion classification with multi-level fusion of Swin-T and ConvNeXt].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals.Sensors (Basel, Switzerland) · 2024Article
- Review
- Article
- The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review.Przeglad gastroenterologiczny · 2024Review
- Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare.Journal of cancer research and clinical oncology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system's computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method's accuracy is improved. Furthermore, the proposed method is computationally efficient.
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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.