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Computer-Assisted Analysis of Colonoscopy: Tube Insertion
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Author(s): Adamya Gaur (Guru Gobind Singh Indraprastha University, East Delhi, India)
Copyright: 2025
Pages: 26
Source title:
Computer-Assisted Analysis for Digital Medicinal Imagery
Source Author(s)/Editor(s): Amit Sinha (ABES Engineering College, Ghaziabad, India), Pranshu Saxena (School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India), Sanjay Kumar Singh (University School of Automation and Robotics,Guru Gobind Singh Indraprastha University, East Delhi, India)and Harikesh Singh (JSS Academy of Technical Education, Noida, India)
DOI: 10.4018/979-8-3693-5226-7.ch005
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Abstract
Multiple organs and tissues are prone to Cancer. This disease, which commences from the colon or rectum, is known as Colorectal Cancer. Colonoscopy is a technique for diagnosis of colon, which plays a crucial role in early detection and treatment of Colorectal Cancer. The book chapter presents multiple supervised and unsupervised techniques like support vector machine (SVM), random forest (RF), Centroid based, Density based clustering, in addition with deep learning techniques such as Convolutional Neural Network (CNN), Atrous Convolution, Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) approaches. The data resources for training of models, along with setup and devices required for colonoscopy are also discussed. Further, metrics for colonoscopy like Adenoma Detection Rate (ADR), Cecal Intubation Rate (CIR) and Adenoma Miss Rate (AMR) are also covered in the chapter. This comprehensive overview serves as a valuable resource for researchers and clinicians aiming to leverage cutting-edge technology in the fight against colorectal cancer.
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