Sridevi, V (2022) MLO and CC view of feature fusion and mammogram classification using deep convolution neural network. MLO and CC view of feature fusion and mammogram classification using deep convolution neural network, 6 (S7). pp. 47194-47205. ISSN 2550-696X

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Abstract

Breast cancer is the most frequent type of cancer in
women all over the world. The improvement of computer aided system
help the radiologist for the effective analysis and diagnosis of breast
cancer. It presents a computational methodology for classifying breast
cancer as normal, benign and malignant from CC and MLO views of
mammogram image. The proposed strategy consists of feature
extraction, multiple view feature fusion and classification. The input
images are fed into feature extraction where convolution neural
network is applied. The CNN is nicely suitable for both feature
extraction, feature fusion and mammogram classification. In this
framework, convolution layer, pooling and activation function are
used as a feature extraction techniques. After the process of feature
extraction, feature fusion is employed by average pooling of CNN. The
feature fusion will increase or maximize the relevant information of
the breast image. Finally obtained features from the fusion are fed
into CNN classifier in which softmax and fully connected layer are
employed as a classifier techniques. The proposed work achieves
98.4% of accuracy to classify the breast cancer from MLO and CC
views using hybrid feature with CNN classifier.

Item Type: Article
Uncontrolled Keywords: Mammogram, Breast Cancer, CNN, Feature Extraction, Feature Fusion, Convolution, Pooling, MLO and CC Views, Classification.
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 06 Oct 2025 05:36
Last Modified: 06 Oct 2025 05:36
URI: https://ir.psgcas.ac.in/id/eprint/2452

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