"The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes (normal, benign and malignant). The performance of the system is evaluated for two different mammogram databases (MIAS and DDSM) in order to assess its robustness. We discuss the changes required in the system, particularly at the level of the image preprocessing and feature extraction. Computational experiments are performed based on different methods for feature extraction, selection and classification. The results indicate an accuracy of 66.95% for the MIAS dataset and 54.1% for DDSM obtained using genetic algorithm based feature selection and Random Forest classification. Keywords: Breast cancer detection, Mammogram classification, GLRLM, Feature selection, Random Forests, MIAS, DDSM. "
Cancer is the illness of the 21th century. With the development of technology some of these lesions became curable, if they are in an early stage. Researchers involved with image processing started to conduct experiments in the field of medical imaging, which contributed to the appearance of systems that can detect and/or diagnose illnesses in an early stage. This paper’s aim is to create a similar system to help the detection of breast cancer. First, the region of interest is defined using filtering and two methods, Seeded Region Growing and Sliding Window Algorithm, to remove the pectoral muscle. The region of interest is segmented using k-means and further used together with the original image. Gray-Level Run-Length Matrix features (in four direction) are extracted from the image pairs. To filter the important features from resulting set Principal Component Analysis and a genetic algorithm based feature selection is used. For classification K-Nearest Neighbor, Support Vector Machine and Decision Tree classifiers are experimented. To train and test the system images of Mammographic Image Analysis Society are used. The best performance is achieved features for directions {45◦ , 90◦ , 135◦ }, applying GA feature selection and DT classification (with a maximum depth of 30). This paper presents a comprehensive analysis of the different combinations of the algorithms mentioned above, where the best performence repored is 100% and 59.2% to train and test accuracies respectively.
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