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Koki Oshio

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Proceedings Article | 1 June 2020 Paper

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

Koki Oshio, Nagito Shichi, Junichi Hasegawa, Tomoyuki Shibata

Proceedings Volume 11515, 115150S (2020) https://doi.org/10.1117/12.2566318

KEYWORDS: Endoscopy, Stomach, Image classification, Diagnostics, Computing systems, Sensors, Image enhancement, CAD systems, Target detection, Convolutional neural networks

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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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