Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …

Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …

Mammography remains the most prevalent imaging tool for early breast cancer screening.
The language used to describe abnormalities in mammographic reports is based on the …

Breast cancer is the most commonly diagnosed cancer among women. Convolutional neural
networks (CNN)-based mammogram classification plays a vital role in early breast cancer …

The BI-RADS report system is widely used by radiologists and clinicians to document
relevant findings in the mammogram exam by using a 6 category final assessment. Deep …

Computer aided diagnosis for mammogram images have seen positive results through the
usage of deep learning architectures. However, limited sample sizes for the target datasets …

This paper explored techniques for diagnosing breast cancer using deep learning based
medical image recognition. X-ray (Mammography) images, ultrasound images, and …

The implementation of deep learning-based computer-aided diagnosis systems for the
classification of mammogram images can help in improving the accuracy, reliability, and cost …

Medical image segmentation is one of the most crucial issues in medical image processing
and analysis. In general, segmentation of the various structures in medical images is …