Date of Award
Spring 4-22-2026
Document Type
Thesis
Publication Status
Version of Record
Submission Date
May 2026
Department
Physics
College Granting Degree
Charles E. Schmidt College of Science
Department Granting Degree
Physics
Degree Name
Professional Science Masters (PSM)
Thesis/Dissertation Advisor [Chair]
Andreas Kyriacou
Abstract
Breast cancer remains one of the leading causes of cancer-related death among women. Early detection through screening mammography is therefore very important. However, mammogram interpretation can be difficult because abnormalities may be subtle and spread across different views. The increasing workload of radiologists can also affect timely analysis. Artificial intelligence (AI) has been widely explored to support mammography interpretation by improving speed and consistency. However, many existing AI methods focus on patch-level or image-level analysis rather than the full patient exam. This limits their ability to capture the complete clinical picture, since important information is distributed across multiple views. In addition, many methods do not provide clear laterality information, which reduces their clinical value.
This thesis presents a patient-level framework for breast cancer classification from multi-view mammograms. The model learns from the full exam rather than isolated patches or single images by using multiple-instance learning. It classifies each patient exam as normal or abnormal and, for abnormal exams, also identifies the abnormal side. The dataset included 1,775 mammography studies, with 1479 normal and 296 abnormal cases. Experimental results showed that Linear Discriminant Analysis (LDA) provided the clearest separation between normal and abnormal studies. Gradient Boosting achieved the highest test accuracy of 88.4% with an Area Under the Curve (AUC) of 69.1%. Random Forest (RF) achieved a similar test accuracy of 88.2% and the highest test AUC of 70.2%. Overall, the proposed framework supports patient-level classification while providing clinically relevant laterality information.
Recommended Citation
Intakhab, Bushra, "PATIENT-LEVEL BREAST CANCER CLASSIFICATION FROM MULTI-VIEW MAMMOGRAPHY WITH SIDE-SPECIFIC EVIDENCE FOR INTERPRETABILITY" (2026). Electronic Theses and Dissertations. 315.
https://digitalcommons.fau.edu/etd_general/315