Sridevi, V (2022) Design and implementation of transfer learned deep CNN with feature fusion for automated mammogram classification. Design and implementation of transfer learned deep CNN with feature fusion for automated mammogram classification, 6 (S6). pp. 3033-3047. ISSN 2550-6978

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Abstract

Radiologists frequently struggle to define mammography
mass lesions, resulting in unneeded breast biopsies to eliminate
suspicions, which adds exorbitant costs to an already overburdened
patient and medical system..Existing models have limited capability
for feature extraction and representation, as well as cancer
classification. Therefore, we built deep Convolution neural networks
based Computer-aided Diagnosis system to assist radiologists in
classifying mammography mass lesions. Here, Two-view LASSO
regression feature fusion and fine-tuned transfer learning network
model VGG16 were applied for identification of mammogram cancer.
First, two independent CNN branches are utilized to extract
mammography characteristics from two different perspectives. Feature
Extraction is performed by fine-tuning pre-trained deep network
models VGG16 which extracts deep convolutional features. Second,
the features of the VGG16 models are serially fused using LASSO
regression. Lastly, the fused features are entered into the Fully
Connected Layer for mammogram classification. The high accuracy of
95.24, senstitivity of 96.11% and AUC score of 97.95% of the
proposed approach revealed that it should be used to enhance clinical
decision-making.

Item Type: Article
Uncontrolled Keywords: -transfer learning, CNN, VGG16, LASSO regression, augmentation, mammogram classification
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 06 Oct 2025 06:55
Last Modified: 06 Oct 2025 06:55
URI: https://ir.psgcas.ac.in/id/eprint/2457

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