Sridevi, V (2023) A combined deep CNN‑lasso regression feature fusion and classifcation of MLO and CC view mammogram image. Int J Syst Assur Eng Manag.

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Abstract

Breast cancer is the most frequent disease among
women, and it is a serious threat to their lives and wellbeing. Due to high population expansion, automatic mammography detection has recently become a critical concern
in the medical industry. The emergence of computer-assisted
systems has aided radiologists in making accurate breast
cancer diagnoses. An automated detection and classifcation system should be implemented to prevent breast cancer
from spreading. Breast densities vary widely among women,
which causes missed cancers. In the case of breast density,
the deep CNN algorithms can signifcantly reduce radiologist workload and improve risk assessment. The goal of
this paper is to ofer a deep learning strategy for identifying
MLO and CC views of breast cancer as malignant, benign,
or normal using an integration of deep convolutional neural networks (CNN) and feature fusion of LASSO (Least
Absolute Shrinkage and Selection Operator) regression. The
proposed method comprises pre-processing, data augmentation, feature extraction, feature fusion, and classifcation.
The generated features were fed into LASSO regression for
the best prediction in this system, which utilized CNN for
feature extraction. The fused features were then transferred
to CNN’s fully connected layer for mammography classifcation. In our experiment, the publically available dataset
CBIS-DDSM (Curated Breast Imaging Subset of DDSM)
was employed. The proposed work gained an accuracy of
99.2%, specifcity of 98.7%, AUC of 99.8%, sensitivity of
99.4%, and FI-score of 98.7%, which is higher than multi
view CNN without a feature fusion based system.

Item Type: Article
Uncontrolled Keywords: CNN · LASSO · Mammogram cancer classifcation · Regression · Feature fusion · Breast cancer
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 04 Jul 2023 05:35
Last Modified: 04 Jul 2023 05:35
URI: http://ir.psgcas.ac.in/id/eprint/1950

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