Automated detection of fundic gland polyps from endoscopic images using SSD

Nagito Shichi, Arata Totsuka, Junichi Hasegawa, Tomoyuki Shibata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Image Technology, IWAIT 2019
EditorsWen-Nung Lie, Phooi Yee Lau, Kazuya Hayase, Lu Yu, Yung-Lyul Lee, Sanun Srisuk, Qian Kemao
ISBN (Electronic)9781510627734
Publication statusPublished - 01-01-2019
EventInternational Workshop on Advanced Image Technology 2019, IWAIT 2019 - Singapore, Singapore
Duration: 06-01-201909-01-2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Workshop on Advanced Image Technology 2019, IWAIT 2019

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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