Work

Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features

Year: 2016

Type: preprint

Abstract: In this paper we report results for recognizing colorectal NBI endoscopic images by using features extracted from convolutional neural network (CNN). In this comparative study, we extract features from different layers from different CNN models, and then train linear SVM classifiers. Experimental results with 10-fold cross validations show that features from first few convolution layers are enough to achieve similar performance (i.e., recognition rate of 95%) with non-CNN local features such as Bag-of-Visual words, Fisher vector, and VLAD.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1608.06709

Authors: Tamaki, Toru, Sonoyama, Shoji, Hirakawa, Tsubasa, Raytchev, Bisser, Kaneda, Kazufumi, Koide, Tetsushi, Yoshida, Shigeto, Mieno, Hiroshi, Tanaka, Shinji

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W2952077276

Cites: 18

Cited by: 9

Related to: 10