Effective deep learning for oral exfoliative cytology classification

Shintaro Sukegawa, Futa Tanaka, Keisuke Nakano, Takeshi Hara, Kazumasa Yoshii, Katsusuke Yamashita, Sawako Ono, Kiyofumi Takabatake, Hotaka Kawai, Hitoshi Nagatsuka, Yoshihiko Furuki

Research output: Contribution to journalArticlepeer-review

Abstract

The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment.

Original languageEnglish
Article number13281
JournalScientific reports
Volume12
Issue number1
DOIs
Publication statusPublished - Dec 2022

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Effective deep learning for oral exfoliative cytology classification'. Together they form a unique fingerprint.

Cite this