The convolutional neural network (CNN) is a promising technique to detect breast cancer
based on mammograms. Training the CNN from scratch, however, requires a large amount …

Deep learning (DL) models are becoming pervasive and applicable to computer vision,
image processing, and synthesis problems. The performance of these models is often …

Deep Learning is vital in medical imaging solutions and clinical applications. However,
multiple reasons, such as data scarcity and imbalance in the medical image dataset, cause …

Abstract Background and Objective: In medical imaging, the scarcity of labeled lesion data
has hindered the application of many deep learning algorithms. To overcome this problem …

Training of a convolutional neural network (CNN) generally requires a large dataset.
However, it is not easy to collect a large medical image dataset. The purpose of this study is …

Mammogram inspection in search of breast tumors is a tough assignment that radiologists
must carry out frequently. Therefore, image analysis methods are needed for the detection …

Breast cancer is a common cancer among women, and screening mammography is the
primary tool for diagnosing this condition. Recent advancements in deep-learning …

We evaluated whether using synthetic mammograms for training data augmentation may
reduce the effects of overfitting and increase the performance of a deep learning algorithm …

Mammography is the primary modality for breast cancer screening, attempting to reduce
breast cancer mortality risk with early detection. However, robust screening less hampered …

Deep learning approaches to breast cancer detection in mammograms have recently shown
promising results. However, such models are constrained by the limited size of publicly …