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We present a novel approach for abnormal breast mass classification from digitized mammography images. The proposed framework exploits rotation invariant uniform Local Binary Pattern (LBP) as texture feature. These features are classified using Support Vector Machine (SVM). In addition, we take advantage of the breast mammograms taken from multiple views or angles. We classify breast scans from ‘cranial-caudal’...
Major Depressive Disorder (MDD) is a serious mental disorder that if untreated not only affects physical health but also has a high risk of suicide. While the neurophysiological phenomena that contribute to the formation of Suicidal Ideation (SI) are still ill-defined, clear links between MDD and cardiovascular disease have been reported. The aim of this study is to extract suitable features from...
Unification of spatial brain dynamics in multiclass brain computer interface (BCI) paradigm reduces computational latencies by using lesser number of electrodes from the sensorimotor regions of the brain. We employ reduced number of channels without compromising performance notably. We apply three spatial filtering methods, i.e., Common Spatial Pattern (CSP), Regularized Common Spatial Pattern (RCSP)...
We present a framework for handwritten Bangla digit recognition using Sparse Representation Classifier. The classifier assumes that a test sample can be represented as a linear combination of the train samples from its native class. Hence, a test sample can be represented using a dictionary constructed from the train samples. The most sparse linear representation of the test sample in terms of this...
We present a Sparse Representation-based Classifier (SRC) that provides superior performance in terms of high Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) in classifying benign and malignant breast lesions captured in ultrasound images. Although such a classifier was proposed for face recognition, it has been proposed in medical diagnosis from ultrasonic images in this work for...
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