TY - GEN
T1 - Automated detection of fundic gland polyps and hyperplastic polyps from endoscopic images using SSD
AU - Oshio, Koki
AU - Shichi, Nagito
AU - Hasegawa, Junichi
AU - Shibata, Tomoyuki
N1 - Publisher Copyright:
© 2020 SPIE CCC.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.1117/12.2566318
DO - 10.1117/12.2566318
M3 - Conference contribution
AN - SCOPUS:85086635371
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - International Workshop on Advanced Imaging Technology, IWAIT 2020
A2 - Lau, Phooi Yee
A2 - Shobri, Mohammad
PB - SPIE
T2 - International Workshop on Advanced Imaging Technology, IWAIT 2020
Y2 - 5 January 2020 through 7 January 2020
ER -