Synthetic Data-Driven Multi-Architecture Framework for Automated Polyp Segmentation Through Integrated Detection and Mask Generation

Ojonugwa Oluwafemi Ejiga Peter; Akingbola Oluwapemiisin; Amalahu Chetachi; Adeniran Opeyemi; Fahmi Khalifa; Md Mahmudur Rahman

doi:10.1117/12.3049369

arXiv · 2508.06170

Synthetic Data-Driven Multi-Architecture Framework for Automated Polyp Segmentation Through Integrated Detection and Mask Generation

Ojonugwa Oluwafemi Ejiga Peter·Akingbola Oluwapemiisin·Amalahu Chetachi·Adeniran Opeyemi·Fahmi Khalifa·Md Mahmudur Rahman

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Abstract

Colonoscopy is a vital tool for the early diagnosis of colorectal cancer, which is one of the main causes of cancer-related mortality globally; hence, it is deemed an essential technique for the prevention and early detection of colorectal cancer. The research introduces a unique multidirectional architectural framework to automate polyp detection within colonoscopy images while helping resolve limited healthcare dataset sizes and annotation complexities. The research implements a comprehensive system that delivers synthetic data generation through Stable Diffusion enhancements together with detection and segmentation algorithms. This detection approach combines Faster R-CNN for initial object localization while the Segment Anything Model (SAM) refines the segmentation masks. The faster R-CNN detection algorithm achieved a recall of 93.08% combined with a precision of 88.97% and an F1 score of 90.98%.SAM is then used to generate the image mask. The research evaluated five state-of-the-art segmentation models that included U-Net, PSPNet, FPN, LinkNet, and MANet using ResNet34 as a base model. The results demonstrate the superior performance of FPN with the highest scores of PSNR (7.205893) and SSIM (0.492381), while UNet excels in recall (84.85%) and LinkNet shows balanced performance in IoU (64.20%) and Dice score (77.53%).

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BibTeX RIS

Ojonugwa Oluwafemi Ejiga Peter, Akingbola Oluwapemiisin, Amalahu Chetachi, Adeniran Opeyemi, Fahmi Khalifa, Md Mahmudur Rahman. 2025-08-08. Synthetic Data-Driven Multi-Architecture Framework for Automated Polyp Segmentation Through Integrated Detection and Mask Generation. https://doi.org/10.1117/12.3049369

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