Innovative Deep Learning Methods for Precancerous Lesion Detection
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2024, Vol 12, Issue 2
Abstract
With the continuous advancement of socio-economic levels and relentless innovation in modern medical technologies, there's been a significant increase in the importance people place on their physiological health, particularly in the context of colorectal cancer—a prevalent malignant tumor that has captivated widespread attention within the medical community for its prevention and treatment. Notably, colorectal polyps, identified as precursors to colorectal cancer, are crucial for early diagnosis and precise detection, serving as fundamental elements in averting the disease and diminishing both its incidence and mortality rates. The swift progression of deep neural network technology in recent years has revolutionized computer-assisted medical diagnosis, especially for the detection of colorectal polyps. Deep learning technology, with its robust capability for feature learning and representation, has emerged as an invaluable aid for physicians, markedly enhancing diagnostic accuracy and efficiency. This study centers on colorectal polyps, striving to develop a detection model with superior accuracy by meticulously analyzing contemporary leading target detection algorithms. By fully exploiting the potent capabilities of deep neural networks, the model aims to boost the precision of colorectal polyp detection significantly, aiding physicians in elevating detection efficiency and simplifying diagnostic processes. By undertaking this research, we aim to make a significant contribution toward more accurate and efficient technological support for the early diagnosis and prevention of colorectal polyps, thereby aiding in the reduction of both the incidence and mortality rates associated with colorectal cancer.
Authors and Affiliations
Yulu Gong, Haoxin Zhang, Ruilin Xu, Zhou Yu, and Jingbo Zhang
Keywords
Related Articles
- EP ID EP745006
- DOI 10.55524/ijircst.2024.12.2.14
- Views 49
- Downloads 0
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