COMPUTER VISION AND MACHINE LEARNING APPROACHES FOR INTERPRETATION OF BREAST HISTOPATHOLOGY

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dc.contributor.authorOdeh, Omar
dc.date.accessioned2024-09-25T10:03:16Z
dc.date.available2024-09-25T10:03:16Z
dc.date.issued2024-09-12
dc.description.abstractBreast cancer, along with other cancer types, represents a major health issue worldwide, especially in regions with limited medical resources, such as Palestine where it’s harder to diagnose and treat cancer patients. The traditional diagnostic methods which rely mainly on the manual examination of histology slides in the lab by experts are both time consuming and require high skills, which often leads to delays in diagnosis and treatment. This research aims to leverage Whole Slide Imaging (WSI) and advanced image processing techniques, including machine learning and deep learning, to develop a tool to support breast cancer diagnosis in Palestine. A collection of histopathology images from breast cancer cases, provided by An-Najah Hospital in Nablus, Palestine, was used in this study. An automated classification model was developed, capable of identifying normal, benign, in situ, and invasive breast cancer tissues at magnifications of x20 and x40. It was empowered using advanced preprocessing techniques to normalize the colors and sizes of the training data. The classification model resulted in a remarkable performance of InceptionV3 and Gradient Boosting models with accuracies of up to 98.7% and 98% respectively in different settings. The same algorithm was applied to a BACH challenge dataset, resulting in a high accuracy of 96.7%, surpassing the results of other studies. The study also covers whole slide image (WSI) analysis, emphasizing the complex approach required for the proper detection of malignancy and tumor size estimation using a customized segmentation approach which resulted in an 84% Jaccard index score. Additionally, it discusses novel approaches in localization of tumor beds and grading of treatment responses, aiming to provide a methodological basis for improving patient care outcomes in challenging environments like Palestine. Keywords: Breast Cancer Diagnosis, Automated Image Analysis, Whole Slide Imaging (WSI), Deep Learning in Pathology, Healthcare in Palestine.
dc.identifier.urihttps://hdl.handle.net/20.500.11888/19581
dc.language.isoen
dc.publisherAn-Najah National University
dc.supervisorToma, Anas
dc.titleCOMPUTER VISION AND MACHINE LEARNING APPROACHES FOR INTERPRETATION OF BREAST HISTOPATHOLOGY
dc.title.alternativeمناهج رؤية الحاسوب و التعلم اآللي لتفسير التشريح المرضي للثدي
dc.typeThesis
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