This study presents a novel deep learning architecture for multi-class classification and localization of abnormalities in medical imaging illustrated through experiments on mammograms. The proposed network combines two branches. One branch is region with newly added normal-region class. Second detection ranking regions relative to one another. Our method enables at full mammogram resolution bo...

This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...

PURPOSE To evaluate the accuracy of a visually lossless, image-adaptive, wavelet-based compression method for achievement of high compression rates at mammography. MATERIALS AND METHODS The study was approved by the institutional review board of the University of South Florida as a research study with existing medical records and was exempt from individual patient consent requirements. Patien...

This paper presents a novel representation of Cartesian genetic programming (CGP) in which multiple networks are used in the classification of high resolution X-rays of the breast, known as mammograms. CGP networks are used in a number of different recombination strategies and results are presented for mammograms taken from the Lawrence Livermore National Laboratory database.

The set of well defined and classified suspicious lesions regions from mammograms database are used as a reference pattern. The similarity measure for reference pattern image and patient mammogram is found by computing the distance between their corresponding feature vectors. The Euclidean distance metric is used to finding the nearest class to patient feature vector what in result mark the aut...