Polyp detection in video colonoscopy using deep learning

Author:

Luca Mihaela1,Ciobanu Adrian1

Affiliation:

1. Institute of Computer Science, Romanian Academy Iaşi Branch, Iaşi, Romania

Abstract

Video colonoscopy automatic processing is a challenge and further development of computer assisted diagnosis is very helpful in correctness assessment of the exam, in e-learning and training, for statistics on polyps’ malignity or in polyps’ survey. New devices and programming languages are emerging and deep learning begun already to furnish astonishing results, in the quest for high speed and optimal polyp detection software. This paper presents a successful attempt in detecting the intestinal polyps in real time video colonoscopy with deep learning, using Mobile Net.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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1. UO-YOLO: Ureteral Orifice Detection Network Based on YOLO and Biformer Attention Mechanism;Applied Sciences;2024-06-12

2. Deep Learning for Relevant Findings in Colonoscopy;Lecture Notes in Networks and Systems;2024

3. Deep Learning in Colonoscopies: Improving Small Polyps Recognition Rate;2022 E-Health and Bioengineering Conference (EHB);2022-11-17

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