Authors : Aruna U; Mallikarjun M Kotur; Mogallapu Rahul; Mohammed Dhanish; Lakshmi Priya B M

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/srdyzvt8

Scribd : https://tinyurl.com/3m26ssjj

DOI : https://doi.org/10.5281/zenodo.14534619

Abstract : Colonoscopy being a critical diagnostic and preventive procedure for colorectal cancer, a major contributor to cancer related deaths globally. Early recognition of polyps at an early stage in the colon is essential for minimizing the progression of cancer. Manual detection during colonoscopy is labor-intensive and vulnerable to human errors such as missed polyps or false diagnoses due to variations in polyp size, shape, and texture. This paper explores a deep learning-based system for automated segmentation and detection of polyps by using advanced architectures like U-Net and Mask R-CNN. These techniques aim to enhance diagnostic precision, reduce clinician workload, and provide real-time feedback during procedures, thereby transforming the landscape of gastrointestinal healthcare.

Keywords : Colonoscopy, Polyp Detection, Segmentation, Deep Learning, CNN, Medical Imaging, Colorectal Cancer.

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