Open Access

ARTICLE

Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

Shahan Yamin Siddiqui^1,2, Iftikhar Naseer³, Muhammad Adnan Khan⁴, Muhammad Faheem Mushtaq⁵, Rizwan Ali Naqvi^6,*, Dildar Hussain⁷, Amir Haider⁸

1 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
2 School of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan
3 Department of Computer Science & Information Technology, Superior University, Lahore, 54000, Pakistan
4 Department of Computer Science, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
5 Department of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan
6 Department of Unmanned Vehicle Engineering, Sejong University, Seoul, 05006, Korea
7 School of Computational Sciences, Korea Institute for Advanced Study (KIAS), 85 HoegiroDongdaemungu, Seoul, 02455, Korea
8 Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, Korea

* Corresponding Author: Rizwan Ali Naqvi. Email:

Computers, Materials & Continua 2021, 67(1), 1033-1049. https://doi.org/10.32604/cmc.2021.013952

Received 26 August 2020; Accepted 17 November 2020; Issue published 12 January 2021

Abstract

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns may be a reason behind its spread among women. However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially. Early diagnosis and initial stage treatment can help increase the survival rate. For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis (CAD) systems. This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion. In multimodal medical imaging fusion, a deep learning approach is applied, obtaining 97.5% accuracy with a 2.5% miss rate for breast cancer prediction. A deep extreme learning machine technique applied on feature-based data provided a 97.41% accuracy. Finally, decision-based fusion applied to both breast cancer prediction models to diagnose its stages, resulted in an overall accuracy of 97.97%. The proposed system model provides more accurate results compared with other state-of-the-art approaches, rapidly diagnosing breast cancer to decrease its mortality rate.

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