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    Computer aided diagnosis of breast tumor segmentation and classification on CT images using machine learning
    (Altınbaş Üniversitesi, 2021) İzaddin, Barish Mohammed İzaddin; Türkben, Ayça Kurnaz
    Computed tomography and Mammography are a screening procedure for early identification of breast tumor symptoms such as masses, calcifications, bilateral asymmetry, and architectural deformation. Because human observers are limited, computers play a critical role in identifying early indications of cancer. The vast range of characteristics that identify abnormalities, as well as the fact that they are frequently indistinguishable from surrounding tissue, make computer-aided breast abnormality detection difficult. This thesis investigates how to use well-known image processing and machine learning techniques to help breast tumor diagnosis using: computed tomography images and Mammography. Therefore, this research aims to develop a computeraided breast tumor screening technique (segmentation and classification) based on mammography images that aid pathologists in their decision-making. Using machine learning methods based on neural networks and logistic regression, a real application is created and constructed, which includes both initial picture processing and subsequent cancer diagnosis. The app is tested on a set of mammography pictures, and the results are displayed and explained in depth. The utilization of a mix of neural networks and logistic regression in breast cancer diagnosis, as well as real breast tumor images (with different location and size of tumors) acquired in collaboration with the Kirkuk Hospital cancer center, distinguishes this thesis.