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Colorectal cancer (CRC) is a leading cause of cancer-related mortality globally, where early detection of
precancerous polyps is vital for patient survival. Although colonoscopy is the gold standard, manual detection
is prone to human error due to clinician fatigue and subtle lesion features. This paper proposes a Deep
Learning–Based Colon Polyp Detection System utilizing Convolutional Neural Networks (CNNs) to
automate the localization of polyps in real time. The system employs transfer learning with the MobileNetV2
architecture, data augmentation, and advanced preprocessing to achieve high sensitivity and specificity.
Experimental observations indicate that the AI-assisted approach reduces the probability of missed polyps,
thereby supporting gastroenterologists in clinical decision-making and improving healthcare efficiency
Keywords:
Colorectal Cancer, Colon Polyp Detection, Deep Learning, CNN, MobileNetV2, Transfer Learning, Computer-Aided Diagnosis
Cite Article:
"Deep Learning - Based Colon Polyp Detection System for Early Colorectal Cancer Screening", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.a624-a625, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603078.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator