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Computer-aided classification system for early endometrial cancer of co-registered photoacoustic and ultrasonic signals
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Paper Abstract

Stage IA endometrial cancer is the only candidate for conservative management. Therefore, early diagnosis of endometrial cancer is very important. Co-registered photoacoustic (PA) and ultrasonic (US) imaging system is available to detect early endometrial cancer (EEC) based on a cylindrical diffuser. To correctly detect and diagnose EEC from FIGO stage IA and stage IB by co-registered PA and US imaging system, a convolutional neural network (CNN) classifier of EEC for co-registered PA and US images was proposed. Activation function ReLU and the dropout technique were used in the CNN classifier. The experiment results show the area under the receiver operating characteristic curve of the proposed algorithm is 0.9998 with a sensitivity of 98.75% and specificity of 98.75%. The CNN classifier could be used in the computer-aided diagnosis for early endometrial cancer of the co-registered PA and US imaging system.

Paper Details

Date Published: 20 November 2019
PDF: 7 pages
Proc. SPIE 11190, Optics in Health Care and Biomedical Optics IX, 111901R (20 November 2019); doi: 10.1117/12.2536709
Show Author Affiliations
Yongping Lin, Xiamen Univ. of Technology (China)
Haiyang Song, Fujian Normal Univ. (China)
Rongsheng Zheng, Xiamen Univ. of Technology (China)
Jianyong Cai, Fujian Normal Univ. (China)
Zhifang Li, Fujian Normal Univ. (China)
Hui Li, Fujian Normal Univ. (China)


Published in SPIE Proceedings Vol. 11190:
Optics in Health Care and Biomedical Optics IX
Qingming Luo; Xingde Li; Ying Gu; Yuguo Tang; Dan Zhu, Editor(s)

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