ArticleDiagnostics (Basel, Switzerland)2021
Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 180 citations in OpenAlex.
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- Automated system utilizing non-invasive technique mammograms for breast cancer detection.BMC medical imaging · 2024Article
- Frequency-hopping along with resolution-turning for fast and enhanced reconstruction in ultrasound tomography.Scientific reports · 2024Article
- Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images.PeerJ. Computer science · 2024Article
- Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.Diagnostics (Basel, Switzerland) · 2023Review
- Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm.Biomimetics (Basel, Switzerland) · 2023Article
- GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Bioinformatics Analysis of the Prognostic Significance of CAND1 in ERα-Positive Breast Cancer.Diagnostics (Basel, Switzerland) · 2022Article
- Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL.Sensors (Basel, Switzerland) · 2022Article
- An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.Sensors (Basel, Switzerland) · 2022Article
- Nano-Based Theranostic Platforms for Breast Cancer: A Review of Latest Advancements.Bioengineering (Basel, Switzerland) · 2022Review
- A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling.Sensors (Basel, Switzerland) · 2022Article
- ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.Sensors (Basel, Switzerland) · 2022Article
- Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.PeerJ. Computer science · 2022Article
- An Optimized Framework for Breast Cancer Classification Using Machine Learning.BioMed research international · 2022Article
- A High-Precision Classification Method of Mammary Cancer Based on Improved DenseNet Driven by an Attention Mechanism.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.
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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.