Voon, Wingates and Hum, Yan Chai and Tee, Yee Kai and Yap, Wun-She and Salim, Maheza Irna Mohamad and Tan, Tian Swee and Mokayed, Hamam and Lai, Khin Wee (2022) Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images. Scientific Reports, 12 (1). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-022-21848-3.
Full text not available from this repository.Abstract
Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 +/- 0.0189 on the cross-validation set and 0.9308 +/- 0.0211on the test set.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Tunku Abdul Rahman Research Fund (Grant No: FRGS/1/2019/ICT04/UTAR/02/1, 8073/Y01 & IPSR/RMC/UTARRF/2022-C1/H01) |
| Uncontrolled Keywords: | Histologic grade; Cancer; Classification |
| Subjects: | R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Nov 2023 04:25 |
| Last Modified: | 14 Nov 2023 04:25 |
| URI: | http://eprints.um.edu.my/id/eprint/40718 |
Actions (login required)
 |
View Item |
