Detection and classification of masses in mammograms using ICA PDF
A. García-MansoThis work is focused on the detection and classification of masses in mammograms. The masses are one of the main indicators of the existence of breast cancer, malignant or benign. For this, here has been developed a detection and classification system of masses in mammograms. This system consists of a set of artificial intelligence techniques such as feature extraction (Principal Component Analysis, PCA, and Independent Component Analysis, ICA) and pattern classification using neural networks (Multilayer Perceptron) and SVM classifiers. The detection and classification of lesions in mammograms is an extremely hard task because of the large variability that the lesions may have, despite being of the same type. In addition, there is the variability that the mammograms may present depending on the breast tissue predominant in the breast and its distribution. Often, this may cause that cancer might appear obscured, masked by the surrounding breast tissue which makes it very difficult to detect.
Computer-Aided Detection and Diagnosis in Mammography
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Detection and classification of the breast …
Detection of breast cancer using independent component analysis. Conference ... tance, but powerful is used in this work to classify benign or ... Identification of masses in digital mammogram using gray level co-occurrence matrices. Article. Image-based features are obtained using Independent Component Analysis (ICA ), ... detection and segmentation of masses in mammograms, as found in [21]. ... More recently, some other papers describing classification of masses in mam-.
Automated Detection, Segmentation and Classification of ... our mass detection, mass segmentation and mass classification methods into a fully automated CAD system for the analysis of masses in mammograms. We validate our methodology on two publicly available datasets (INbreast and DDSM-BCRP) using different performance measures
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The task of detect and classify mammary lesions using mammograms is highly dependent on the feature extraction stage, in which the regions of interest, clinically ...