Purpose Due to the low contrast, blurry boundaries, and large amount of shadows in breast
ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep …

Nowadays, it is important for the detection of ultrasound images of breast tumors. In this
paper, a new ultrasonic image feature extraction algorithm combining edge-based features …

The performance of supervised classification algorithms is highly dependent on the quality
of training data. Ambiguous training patterns may misguide the classifier leading to poor …

With advancements in machine learning algorithms and computer aided diagnostic (CAD)
systems, the performance of automated analysis of radiological images has improved …

Selection of relevant and appropriate features to characterize breast patterns is of
paramount importance in breast tissue representation and classification in machine learning …

Breast cancer is one of the leading causes of female cancer deaths. Early diagnosis with
prophylactic may improve the patients' prognosis. So far ultrasound (US) imaging has been …

Breast cancer is the most commonly occurring cancer in women worldwide. While
mammography remains the gold standard in breast cancer screening, ultrasound is an …

Feature extraction and classification plays a crucial role in the automated analysis of breast
ultrasound (BUS) images. Due to varying sonographic characteristics of benign and …

For screening breast tumors, different imaging modalities like ultrasound, mammography,
computed tomography (CT), magnetic resonance imaging (MRI) have been utilized …

Objectives This work focused on extracting novel and validated digital high‐throughput
features to present a detailed and comprehensive description of the American College of …