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Automated detection of fundic gland polyps from endoscopic images using SSD
Author(s): Nagito Shichi; Arata Totsuka; Junichi Hasegawa; Tomoyuki Shibata
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Paper Abstract

In stomach lesion screening, endoscopic images provide the most effective diagnostic information. However, in the most of lesions at the initial stage, the sign of existence is hard to appear on endoscopic images, and also there is the difference in operations of endoscopes and observation of images in real time among individual medical doctors. Therefore, development of a computer aided diagnostic system (CAD system) for endoscopic images is required. In this study, we propose a method for automated detection of fundic gland polyps from endoscopic images using an object detection algorithm named SSD (Single Shot MultiBox Detector) which is one of CNN (Convolutional Neural Network). SSD used here has 20 of convolution layers and 6 of pooling layers, and the input image size is 300x300. In the experiment, 73 practical fundic gland polyp images were used. To compensate for lack of training images, augmentation was performed using image rotation and edge enhancement. We trained 8751 training images and 2188 verification images. Also, as a preprocessing, highlight areas were removed automatically from all images including both training and test samples. As a result, 94.7% of TP (true positive) rate for 73 fundic gland polyp images was obtained by using our learned SSD.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110492O (22 March 2019); doi: 10.1117/12.2521431
Show Author Affiliations
Nagito Shichi, Chukyo Univ. (Japan)
Arata Totsuka, Chukyo Univ. (Japan)
Junichi Hasegawa, Chukyo Univ. (Japan)
Tomoyuki Shibata, Fujita Health Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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