Date on Master's Thesis/Doctoral Dissertation

5-2025

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering, PhD

Committee Chair

Ali, Asem

Committee Member

Zurada, Jacek

Committee Member

Yampolskiy, Roman

Committee Member

McIntyre, Michael

Author's Keywords

CT; A.I; deep learning; CNN; polyp detection

Abstract

Colon cancer, also known as colorectal cancer, is a significant health concern, with increasing incidence rates, particularly among individuals under 50. This rise has led experts to recommend the introduction of regular screenings at 45 years of age for adults at average risk. Early detection through such screenings can identify precancerous polyps, allowing their removal before they develop into cancer. This proactive approach has the potential to reduce colorectal cancer deaths by up to 60%. In addition, research indicates that people diagnosed before age 50 have better survival rates, which emphasizes the importance of early diagnosis. Therefore, adhering to recommended screening guidelines and promptly addressing concerns can significantly improve outcomes and save lives. This study proposes a framework to detect and localize colon cancer indicators (polyps) in 3D medical images, which has been applied to help radiologists read computed tomography (CT) scans and identify candidate CT slices with colonic polyps. This work’s main benefit is introducing AI-based colon abnormalities detection framework that the radiologist could miss by using different approaches and modalities. In this work, we propose an automatic detection approach for colorectal polyps consisting of two cascade stages. In the first stage, a Convolutional Neural Network (CNN) model is trained to detect polyps in axial CT slices; a CNN model has been fed by the segmented colon wall CT slices instead of the original CT slices. Using segmented images as input to the CNN model has drastically improved detection and localization results; for example, the mean average precision (mAP) for detection increases by 36%. To reduce false positives generated by the detector, the second stage classifier is deployed to exploit the different views of CT scans instead of the axial view only. So, the classifier is trained using the 2D images of axial views, i.e., the candidate polyps generated by the detector, as well as their corresponding 2D images of sagittal and coronal views. Different experiments were conducted to evaluate the proposed work, including the Fly-In approach to visualize and detect polyps within the 3D colon model, which was successfully used to train a CNN-based model that detects polyps with mAP∼ 97.1%. The second approach is to use the axial CT view after removing other organs except the colon and using A.I model to detect polyps within the 2D axial view with mAP∼ 86%. Finally, the MVN classification approach successfully identified polyps after the classification stage with an area under curve (AUC) ∼ 95.27% in our private dataset and 86% in the public dataset.

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