An Optimized Framework for Breast Cancer Classification Using Machine Learning.
Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of bre...
| Publicado en: | BioMed Research International pp. 1 - 19 |
|---|---|
| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/18/2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155334034&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155334034 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/18/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155334034 155334034 155334034 10.1155/2022/8482022 155334034 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Optimized Framework for Breast Cancer Classification Using Machine Learning. aug: au: Michael, Epimack Ma, He Li, Hong Qi, Shouliang affil: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China sug: subj: Breast Neoplasms Classification Image Processing, Computer Assisted Machine Learning Algorithms Evaluation Conceptual Framework Human Breast Ultrasonography Breast Neoplasms Diagnosis Diagnosis, Computer Assisted Probability Validity Sensitivity and Specificity Reproducibility of Results ab: Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|