ArticleBioMed research international2022
An Optimized Framework for Breast Cancer Classification Using Machine Learning.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Construction of risk prediction models for psoriasis based on 45 dietary nutrients using machine learning and SHAP analysis.Asia Pacific journal of clinical nutrition · 2026Article
- Integrating Large Language Models with Deep Learning for Breast Cancer Treatment Decision Support.Diagnostics (Basel, Switzerland) · 2026Article
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- A comparative analysis of parametric survival models and machine learning methods in breast cancer prognosis.Scientific reports · 2025Article
- Innovative breast cancer detection using a segmentation-guided ensemble classification framework.Biomedical engineering letters · 2025Article
- Patho-Net: enhancing breast cancer classification using deep learning and explainable artificial intelligence.American journal of cancer research · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.The journal of allergy and clinical immunology. In practice · 2024Article
- Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- BCDiagnostics (Basel, Switzerland) · 2023Article
- Complete Breast Cancer Detection and Monitoring System by Using Microwave Textile Based Antenna Sensors.Biosensors · 2023Article
- Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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