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Recently, breast cancer is one of the most popular cancers that women could suffer from. The gravity and seriousness of breast cancer can be evidenced by the fact that the mortality rates associated with it are the second highest after lung cancer. For the treatment of breast cancer, Mammography has emerged as the one whose modality when it comes to the defection of this cancer is most effective despite the challenges posed by dense breast parenchyma. In this regard, computer-aided diagnosis (CADe) leverages the mammography systems’ output to facilitate the radiologist’s decision. It can be defined as a system that makes a similar diagnosis to the one done by a radiologist who relies for his/her interpretation on the suggestions generated by a computer after it analyzed a set of patient radiological images when making. Against this backdrop, the current paper examines different ways of utilizing known image processing and techniques of machine learning detection of breast cancer using CAD – more specifically, using mammogram images. This, in turn, helps pathologist in their decision-making process. For effective implementation of this methodology, CADe system was developed and tested on the public and freely available mammographic databases named MIAS database. CADe system is developed to differentiate between normal and abnormal tissues, and it assists radiologists to avoid missing breast abnormalities. The performance of all classifiers is the best by using the sequential forward selection (SFS) method. Also, we can conclude that the quantization grey level of (gray-level co-occurrence matrices) GLCM is a very significant factor to get robust high order features where the results are better with L equal to the size of ROI. Using an enormous number of several features assist the CADe system to be strong enough to distinguish between the different tissues.
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