27 February 2018 Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis
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Abstract
This paper presents a new classification method for endocytoscopic images. Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endo-scopic views of polyps during colonoscopy. However, endocytoscopic image diagnosis requires higher experiences for physicians. An automated pathological diagnosis system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new automated endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images. This method consists of two classification steps. At the first step, we classify an input image by support vector machine. We forward the image to the second step if the confidence of the first classification is low. At the second step, we classify the forwarded image by convolutional neural network. We reject the input image if the confidence of the second classification is also low. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 16,000 and 4,000 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 93.4% with small rejection rate 9.3% even for difficult test data.
Conference Presentation
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Hayato Itoh, Hayato Itoh, Yuichi Mori, Yuichi Mori, Masashi Misawa, Masashi Misawa, Masahiro Oda, Masahiro Oda, Shin-ei Kudo, Shin-ei Kudo, Kensaku Mori, Kensaku Mori, "Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis ", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057516 (27 February 2018); doi: 10.1117/12.2293495; https://doi.org/10.1117/12.2293495
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