[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9717/kmms.2021.24.7.849

Multi-class Classification of Histopathology Images using Fine-Tuning Techniques of Transfer Learning

Ikromjanov, Kobiljon (Dept of Digital Anti-Aging Healthcare, u-AHRC, Inje University)
Bhattacharjee, Subrata (Dept of Computer Engineering, u-AHRC, Inje University)
Hwang, Yeong-Byn (Dept of Computer Engineering, u-AHRC, Inje University)
Kim, Hee-Cheol (Dept of Digital Anti-Aging Healthcare, u-AHRC, Inje University)
Choi, Heung-Kook (Dept of Computer Engineering, u-AHRC, Inje University)

Publication Information

Journal of Korea Multimedia Society / v.24, no.7, 2021 , pp. 849-859 More about this Journal

Abstract

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

Keywords

Transfer Learning; Fine-tuning; Deep Learning; Prostate Cancer;

Citations & Related Records

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