Background Machine learning (ML) has become a vital part of medical imaging research.
ML methods have evolved over the years from manual seeded inputs to automatic …

Background: Feature extraction is a key issue in designing a computer aided diagnosis
system. Recent researches on breast cancer diagnosis have reported the effectiveness of …

This paper presents a method for classification of normal and abnormal tissues in
mammograms using a deep learning approach. VGG-16 CNN deep learning architecture …

This paper presents a new approach for breast cancer classification using time series
analysis. In particular, the region of interest (ROI) in mammogram images is classified as …

Mammography is an inexpensive and non-invasive method through which one can
diagnose breast cancer in its early stages. As these images need interpretation by a …

In this paper, fusion of texture features to improve classification accuracy by false positive
reduction in mammograms is proposed. The method uses texture features obtained from …

Masses are mammographic nonpalpable signs of breast cancer. These masses could be
detected using screening mammography. This paper proposed a system utilizing orthogonal …

In this work, a new problem of script identification named artistic multi-character script
identification has been addressed. Two types of datasets of artistic documents/images …

It is still unexplored territory to automate the distinction of breast tumors using breast
ultrasound (US) images. The parametric image-based approach described in this research …

This paper presents a feature fusion technique that can be used for classification of ROIs in
breast cancer into normal and abnormal classes. The texture features are extracted using …