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SINHA,H.. KARNATI, AGGARWAL, G.M. DUTTA, M.K. MEZINA, A., BURGET, R.
Original Title
DMRBNet: Dilated Multi-scale Residual Block-based Deep Network for Detection of Breast Cancer from MRI Images
Type
conference paper
Language
English
Original Abstract
Breast cancer (BC) is a common type of cancer that develops from breast tissue cells. Early detection is critical, and mammography is an important tool for this. A biopsy is indicated for lesions with a risk of malignancy of more than 2%, however, only a tiny number of them are confirmed to be malignant. Magnetic Resonance Imaging (MRI) is employed to eliminateunneeded biopsies, but it is a sophisticated and time-consuming operation requiring specialized knowledge. To improve breast cancer diagnosis, a computer-aided diagnostic system using MRI images was developed. The system utilizes a novel neural network called dilated multi-scale residual block-based convolutional neural network (DMRBNet), which effectively extracts features from various image regions. Compared to seven recent advanced approaches, DMRBNet demonstrated superior performance on the BC-MRI dataset. The accuracy of the network is 98.57%, and the error rate is 0.1005. These findings highlight its potential for medical and industrial applications in breast cancer detection.
Keywords
Breast cancer;convolutional neural network;residual connection;MRI images;classification;multi-scale features
Authors
SINHA,H..; KARNATI, AGGARWAL, G.;M.; DUTTA, M.K.; MEZINA, A., BURGET, R.
Released
30. 10. 2023
Location
Ghent
ISBN
979-8-3503-9328-6
Book
15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
Pages from
38
Pages to
43
Pages count
5
BibTex
@inproceedings{BUT185377, author="SINHA,H.. and KARNATI, AGGARWAL, G. and M. and DUTTA, M.K. and MEZINA, A., BURGET, R.", title="DMRBNet: Dilated Multi-scale Residual Block-based Deep Network for Detection of Breast Cancer from MRI Images", booktitle="15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)", year="2023", pages="38--43", address="Ghent", isbn="979-8-3503-9328-6" }