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Improved bald eagle search optimization with entropy-based deep feature fusion model for breast cancer diagnosis on digital mammograms

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Abstract

Early and accurate diagnosis of breast cancer (BC) using digital mammograms can improve disease detection accuracy. Medical images can be detected, segmented, and classified for the design of computer-aided diagnosis (CAD) models which assist radiologists in accurately diagnosing breast lesions. Therefore, this study proposes an Improved Bald Eagle Search Optimization with Entropy-based Deep Feature Fusion (IBESO-EDFFM) model for BC Diagnosis on Digital Mammograms. The goal of the IBESO-EDFFM technique lies in the proper detection and segmentation of BC using feature fusion and hyperparameter tuning concepts. For the feature extraction process, the IBESO-EDFFM technique employs an entropy-based feature fusion process, comprising three deep learning models namely Capsule Network (CapsNet), Inception v3, and EfficientNet. Besides, Improved Bald Eagle Search Optimization (IBESO) with Bidirectional-Quasi Recurrent Neural Network (BiQRNN) is utilized for the identification and classification of breast cancer. Finally, a fully convolutional network with RMSProp optimizer is exploited for the segmentation of abnormal regions from the classified images. The experimental result analysis of the IBESO-EDFFM technique is tested on the MIAS mammography dataset from the Kaggle repository and the comparative results show the better performance of the IBESO-EDFFM technique over recent approaches with maximum accuracy of 98.96%.

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Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, India

    P. Ashwini, N. Suguna & N. Vadivelan

  2. Computer Science and Engineering, Teegala Krishna Reddy Engineering College, Hyderabad, India

    N. Vadivelan

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  1. P. Ashwini
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  2. N. Suguna
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Ashwini, P., Suguna, N. & Vadivelan, N. Improved bald eagle search optimization with entropy-based deep feature fusion model for breast cancer diagnosis on digital mammograms. Multimed Tools Appl 83, 41785–41803 (2024). https://doi.org/10.1007/s11042-023-17144-5

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