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In mammography diagnosis systems, high False Negative Rate (FNR) has always been a significant problem since a false negative answer may lead to a patient's death. This paper is directed towards the development of a novel Computer-aided Diagnosis (CADx) system for the diagnosis of breast masses. It aims at intensifying the performance of CADx algorithms as(More)
Classification of breast abnormalities such as masses is a challenging task for radiologists. Computer-aided Diagnosis (CADx) technology may enhance the performance of radiologists by assisting them in classifying patterns into benign and malignant categories. Although Neural Networks (NN) such as Multilayer Perceptron (MLP) have drawbacks, namely long(More)
This paper presents our recent development of a classification algorithm for identification of breast cancer margins measured by hyperspectral imaging for the purpose of lowering the number of missed positive margins in breast cancer lumpectomy. After extracting Fourier coefficient selection features and reducing the dimensionality of hyperspectral image(More)
This paper presents improvements made to the previously developed noise classification path of the environment-adaptive cochlear implant speech processing pipeline. These improvements consist of the utilization of subband noise features together with a random forest tree classifier. Three commonly encountered noise environments of babble, street, and(More)
Breast cancer has become a common health problem in developed and developing countries during the last decades and also the leading cause of mortality in women each year. Mammogram is a special x-ray examination of the breast made with specific x-ray equipment that can often find tumors too small to be felt. In this paper, the classification of(More)
This paper presents an improved environment-adaptive noise suppression solution for the cochlear implants speech processing pipeline. This improvement is achieved by using a multi-band data-driven approach in place of a previously developed single-band data-driven approach. Seven commonly encountered noisy environments of street, car, restaurant, mall, bus,(More)
This paper presents the real-time implementation and field testing of an app running on smartphones for classifying noise signals involving subband features and a random forest classifier. This app is compared to a previously developed app utilizing mel-frequency cepstral coefficients features and a Gaussian mixture model classifier. The real-time(More)