Breast cancer histology images classification: Training from scratch or transfer learning?

• Published in: ICT express Vol. 4; no. 4; pp. 247 - 254
• Main Authors: Shallu, Mehra, Rajesh
• Format: Journal Article
• Language: English
• Published: Elsevier 01.12.2018; 한국통신학회
• Subjects: 전자/정보통신공학
• ISSN: 2405-9595; 2405-9595
• DOI: 10.1016/j.icte.2018.10.007

We demonstrated the ability of transfer learning in comparison with the fully-trained network on the histopathological imaging modality by considering three pre-trained networks: VGG16, VGG19, and ResNet50 and analyzed their behavior for magnification independent breast cancer classification. Concurrently, we examined the effect of training–testing data size on the performance of considered networks. A fine-tuned pre-trained VGG16 with logistic regression classifier yielded the best performance with 92.60% accuracy, 95.65% area under ROC curve (AUC), and 95.95% accuracy precision score (APS) for 90%–10% training–testing data splitting. Layer-wise fine-tuning and different weight initialization schemes can be a future aspect of this study. Keywords: Breast cancer, Histopathological images, Convolutional neural network, Full training, Transfer learning