ArticleMedical & biological engineering & computing2018
A novel and reliable computational intelligence system for breast cancer detection.
Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling.Healthcare (Basel, Switzerland) · 2022Article
- Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver.Diagnostics (Basel, Switzerland) · 2022Article
- Modeling long-range dependencies for weakly supervised disease classification and localization on chest X-ray.Quantitative imaging in medicine and surgery · 2022Article
- Research of the Distribution of Tongue Features of Diabetic Population Based on Unsupervised Learning Technology.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold.Medical & biological engineering & computing · 2021Article
- [Application and Progress of Convolutional Neural Network-based Pathological Diagnosis of Gastric Cancer].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2021Review
- Self-Organising Map Based Framework for Investigating Accounts Suspected of Money Laundering.Frontiers in artificial intelligence · 2021Article
- The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey.Journal of personalized medicine · 2020Review
- An effective approach for breast cancer diagnosis based on routine blood analysis features.Medical & biological engineering & computing · 2020Article
- DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.Medical & biological engineering & computing · 2020Article
- Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine.Medical & biological engineering & computing · 2020Article
- A reliable method for colorectal cancer prediction based on feature selection and support vector machine.Medical & biological engineering & computing · 2019Article
- Attitudes Of Chinese Cancer Patients Toward The Clinical Use Of Artificial Intelligence.Patient preference and adherence · 2019Article
- The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide.Technology in cancer research & treatmentReview
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-organizing map (SOM) and complex-valued neural network (CVNN), for reliable detection of breast cancer. The dataset used in this paper consists of 822 patients with five features (patient's breast mass shape, margin, density, patient's age, and Breast Imaging Reporting and Data System assessment). The proposed model was used for the first time and can be categorized in two stages. In the first stage, considering the input features, SOM technique was used to cluster the patients with the most similarity. Then, in the second stage, for each cluster, the patient's features were applied to complex-valued neural network and dealt with to classify breast cancer severity (benign or malign). The obtained results corresponding to each patient were compared to the medical diagnosis results using receiver operating characteristic analyses and confusion matrix. In the testing phase, health and disease detection ratios were 94 and 95%, respectively. Accordingly, the superiority of the proposed model was proved and can be used for reliable and robust detection of breast cancer.
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