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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’...
Breast cancer is the most lethal form of cancer in women after lung cancer. Early detection of cancer is likely to improve the patient's ability to deal with the disease and live further. Ultrasound imaging technique is one of the available tools for cancer diagnosis. Over the years, several high precision features were suggested by researchers to distinguish between malignant and benign lesions....
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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