Automated detection of fundic gland polyps and hyperplastic polyps from endoscopic images using SSD

Koki Oshio, Nagito Shichi, Junichi Hasegawa, Tomoyuki Shibata

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

Abstract

In recent years, for reducing diagnostic burdens in stomach screening, a computer aided diagnostic system (CAD system) for endoscopic stomach images is required. In our previous study, an automated polyp detection method from endoscopic images using the SSD (Single Shot MultiBox Detector) has been developed with 93.7% of detection rate. However, the detection target of this method has been limited only to fundic gland polyp. In this paper, we propose a method for automated detection and classification of two different types of polyp; fundic gland polyp (FGP) and hyperplastic polyp (HP) from endoscopic images using the SSD. In the experiment, 71 and 96 practical endoscopic images of FGP and HP were used. For training of SSD, 11210 and 5053 training images of FGP and HP were generated by data augmentation, respectively, and 20% of training images were automatically selected and used as verification images. As a result for test samples including 132 polyps (69 FGPs and 63 HPs), the detection rate for entire polyps was 96.2% (127/132), and the classification rate for two types of polyp was 88.6% (117/132). The number of false positive was only one all through the experiment.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2020
EditorsPhooi Yee Lau, Mohammad Shobri
PublisherSPIE
ISBN (Electronic)9781510638358
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventInternational Workshop on Advanced Imaging Technology, IWAIT 2020 - Yogyakarta, Indonesia
Duration: 05-01-202007-01-2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11515
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Workshop on Advanced Imaging Technology, IWAIT 2020
CountryIndonesia
CityYogyakarta
Period05-01-2007-01-20

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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