[2J3] [General Session] 13. AI Application

[2J3-01] Objective evaluation for the cystoscopic diagnosis of bladder cancer using transfer learning

〇Yutaro Hoshino^1,2, Hidenori Sakanashi², Masahiro Murakawa², Nagatsugu Yamanouchi¹, Hirokazu Nosato^1,2 (1. Graduate School of Toho University, 2. Artificial Intelligence Research Center, National Institute of Adanced Industrial Science and Technology)

Keywords:Cystoscopy, Transfer Learning, Diagnosis Support, Deep Learning, Computer Vision

This paper proposes an objective evaluation method for cystoscopic diagnosis of bladder cancer based on transfer learning using pre-trained DCNN (Deep Convolutional Neural Network) model. In the proposed method, lesion detection with comparatively fewer cystoscope images was realized by using the DCNN model pre-trained with a large amount of general images as fixed feature extractor and by learning the extracted feature vectors with subsequent classifiers. In this paper, in order to verify the effectiveness of the proposed method, experiments using actual bladder cystoscopic images were performed. As a result of the experiment, the proposed method achieved 95.7% in sensitivity and 93.3% in specificity in the two-class classification of normal and flat lesions which are difficult to distinguish, and showed the effectiveness for the cystoscopic diagnosis of bladder cancer.