In this paper, three Double Machine Learning (DML) models are proposed to enhance the accuracy of breast cancer detection using machine learning techniques using breast cancer detection dataset. The DML models learn the primary features using machine...
BACKGROUND AND OBJECTIVES: Early detection of breast cancer through mammography significantly improves survival rates. However, high false positive and false negative rates remain a challenge. Deep learning-based computer-aided diagnosis systems can ...
OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.
Breast cancer is the second leading cause of cancer-related deaths among women, following lung cancer, as of 2024. Conventional cancer diagnosis relies on the manual examination of biopsied tissues by pathologists, a time-consuming process that may v...
Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical...
The expansion rate of medical data during the past ten years has rapidly expanded due to the vast fields. The automated disease diagnosis system is proposed using a deep learning (DL) algorithm, which automates and helps speed up the process efficien...
This study integrates multimodal metabolomic data from three platforms-LC-MS, GC-MS, and NMR-to systematically identify biomarkers distinguishing breast cancer subtypes. A feedforward attention-based deep learning model effectively selected 99 signif...
Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. The manual classification of these is a hectic and time-consuming pr...
Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients w...
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types,...
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