Comparison on the deep learning performance of a field of view variable color images of uterine cervix

Ye Rang Park; Yu Jin Seol; Young Jae Kim; Kwang Gi Kim

doi:10.1117/12.2588598

20 April 2021 Comparison on the deep learning performance of a field of view variable color images of uterine cervix

Ye Rang Park, Yu Jin Seol, Young Jae Kim, Kwang Gi Kim

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 117920I (2021) https://doi.org/10.1117/12.2588598
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

Abstract

The purpose of this study is to use a deep learning model to identify the possibility of lesions in the cervix and to evaluate the efficient image preprocessing in order to diagnose diverse types of cervix in form. The study used 4,107 normal photographs of uterine cervix and 6,285 abnormal photographs of uterine cervix. Under the same size condition, to see if which method is more effective to performance either removal of the vaginal wall area or diagnosing cervical cancer including the vaginal wall area, two types of image preprocessing were resized to square. The average accuracy of cropped cases is 94.15%. The average accuracy of the filled cases is 93.41%.

Citation Download Citation

Ye Rang Park, Yu Jin Seol, Young Jae Kim, and Kwang Gi Kim "Comparison on the deep learning performance of a field of view variable color images of uterine cervix", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 117920I (20 April 2021); https://doi.org/10.1117/12.2588598

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