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Comparative Analysis to Identify Machine Learning Methods in Predicting Benign-Malignant Types of Breast Cancer

https://doi.org/10.2139/ssrn.4772541
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Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR
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Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique
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Feature fusion and Ensemble learning-based CNN model for mammographic image classification
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Prediction of Surgical Upstaging Risk of Ductal Carcinoma In Situ Using Machine Learning Models
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Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review
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Impact of breast density on diagnostic accuracy in digital breast tomosynthesis versus digital mammography: results from a European screening trial
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Interplay between ESR1/PIK3CA codon variants, oncogenic pathway alterations and clinical phenotype in patients with metastatic breast cancer (MBC): comprehensive circulating tumor DNA (ctDNA) analysis
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Measures of Outcome in Metastatic Breast Cancer: Insights From a Real-World Scenario
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Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis
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Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images.
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Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture
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Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images
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