TY - GEN
T1 - Lightweight Deep Network for the Classification of Breast Cancer Histopathological Images
AU - Waqas, Muhammad
AU - Maul, Tomas
AU - Liao, Iman Yi
AU - Ahmed, Amr
N1 - Funding Information:
This work would not have been possible without the financial support of the University of Malakand, Higher Education Commission (Government of Pakistan) and the University of Nottingham Malaysia.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/12/21
Y1 - 2022/12/21
N2 - Out of all cancers among female patients, breast cancer is on top in terms of prevalence. Many deep learning approaches have been investigated for the classification of histopathological images of breast cancer, however most tend to be either too complex or too large to adopt at a clinical level. In this study we propose a lightweight network, based on knowledge distillation, which performs almost the same as the teacher network but with significantly fewer parameters. Our student network consists of inverted residual blocks of MobileNetV2 and the ghost module. We performed our experiments on BreakHis, BACH and Kaggle breast cancer histopathological imaging datasets. The results show that different versions of our proposed lightweight student architecture perform with similar accuracy levels compared with the teacher network, while using significantly fewer parameters.
AB - Out of all cancers among female patients, breast cancer is on top in terms of prevalence. Many deep learning approaches have been investigated for the classification of histopathological images of breast cancer, however most tend to be either too complex or too large to adopt at a clinical level. In this study we propose a lightweight network, based on knowledge distillation, which performs almost the same as the teacher network but with significantly fewer parameters. Our student network consists of inverted residual blocks of MobileNetV2 and the ghost module. We performed our experiments on BreakHis, BACH and Kaggle breast cancer histopathological imaging datasets. The results show that different versions of our proposed lightweight student architecture perform with similar accuracy levels compared with the teacher network, while using significantly fewer parameters.
KW - Breast cancer
KW - Histopathological Images
KW - Knowledge Distillation
UR - http://www.scopus.com/inward/record.url?scp=85146216847&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146216847&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI56279.2022.9980033
DO - 10.1109/CISP-BMEI56279.2022.9980033
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85146216847
SN - 9781665488877
T3 - Proceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
BT - Proceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
A2 - Chen, Xin
A2 - Cao, Lin
A2 - Li, Qingli
A2 - Wang, Yan
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
Y2 - 5 November 2022 through 7 November 2022
ER -
