Chakrabarty, AmitabhaProkriti, Hasin ArmanAyesha, Tasnia Jannat2025-09-162025-09-1620252025-06ID 20201092ID 21301611http://hdl.handle.net/10361/26751Cataloged from PDF version of thesis.Includes bibliographical references (pages 40-41).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality worldwide, with early detection of colorectal polyps playing a vital role in reducing its impact. Manual identification of polyps during colonoscopy is often limited by human error, variability in polyp appearance, and real-time diagnostic constraints. This work explores a deep learning-based approach to automatic polyp segmentation in endoscopic images, aiming to enhance detection accuracy and reduce diagnostic delays. Multiple convolutional and transformer-based segmentation models are implemented to learn pixel-level features associated with polyp structures. The publicly available Kvasir-SEG dataset is utilized and further augmented to improve training diversity. A specialized boundary-aware loss function is introduced to address the challenge of ambiguous polyp borders. The proposed method emphasizes the importance of robust preprocessing, architectural design, and edge-focused optimization to advance the reliability of computer-aided diagnosis systems in clinical gastroenterology.50 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Colorectal cancerPolyp detectionDisease detectionPolyp segmentationDeep learningMedical imagesImage processingAutomated diagnosisUNET++ResUnet++Data augmentationImage analysisColonoscopyDiagnostic imaging--Data processing.Diagnosis--Technological innovations.Colon (Anatomy)--Cancer--Early detection.Deep learning (Machine learning).Thesis