Breast Cancer Classification Using Equivariance Transition in Group Convolutional Neural Networks

Breast Cancer Classification Using Equivariance Transition in Group Convolutional Neural Networks

In computer vision, rotation equivariance and translation invariance are properties of a representation that preserve the geometric structure of a transformed input. These properties are achieved in Convolutional Neural Networks (CNNs) through data augmentation. However, achieving these properties r...

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Bibliographic Details
Main Authors: Zaharaddeen Sani, Rajesh Prasad, Ezzeddin Kamil Mohamed Hashim
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Breast cancer
convolutional neural network
discrete cosine transform
group convolution
equivariance transition
image processing
Online Access:https://ieeexplore.ieee.org/document/10061394/

Internet

https://ieeexplore.ieee.org/document/10061394/

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