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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

Sahar Arooj1, Muhammad Farhan Khan2, Tariq Shahzad3, Muhammad Adnan Khan4,5,6, Muhammad Umar Nasir7, Muhammad Zubair1, Atta-ur-Rahman8, Khmaies Ouahada3,*

1 Faculty of Computing, Riphah International University, Islamabad, 45000, Pakistan
2 Department of Forensic Sciences, University of Health Sciences, Lahore, 54000, Pakistan
3 Department of Electrical and Electronic Engineering Science, University of Johannesburg, P.O. Box 524, Johannesburg, 2006, South Africa
4 School of Computing, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates
5 Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
6 Department of Software, Faculty of Artificial Intelligence & Software, Gachon University, Seongnam, Gyeonggido, 13120, Korea
7 Department of Computer Science, Bahria University, Lahore Campus, Lahore, 54000, Pakistan
8 Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441, Saudi Arabia

* Corresponding Author: Khmaies Ouahada. Email: email

Computers, Materials & Continua 2023, 77(3), 2813-2831. https://doi.org/10.32604/cmc.2023.043013

Abstract

Breast cancer (BC) is the most widespread tumor in females worldwide and is a severe public health issue. BC is the leading reason of death affecting females between the ages of 20 to 59 around the world. Early detection and therapy can help women receive effective treatment and, as a result, decrease the rate of breast cancer disease. The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body. Tumors are classified as benign or malignant, and the absence of cancer in the breast is considered normal. Deep learning, machine learning, and transfer learning models are applied to detect and identify cancerous tissue like BC. This research assists in the identification and classification of BC. We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification (BCIC), which are machine learning-based models, by evaluating them in the form of comparative research. We used 3 datasets, A, B, and C. We fuzzed these datasets and got 2 datasets, A2C and B3C. Dataset A2C is the fusion of A, B, and C with 2 classes categorized as benign and malignant. Dataset B3C is the fusion of datasets A, B, and C with 3 classes classified as benign, malignant, and normal. We used customized AlexNet according to our datasets and BCIC in our proposed model. We achieved an accuracy of 86.5% on Dataset B3C and 76.8% on Dataset A2C by using AlexNet, and we achieved the optimum accuracy of 94.5% on Dataset B3C and 94.9% on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate. We proposed fuzzed dataset model using transfer learning. We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

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APA Style
Arooj, S., Khan, M.F., Shahzad, T., Khan, M.A., Nasir, M.U. et al. (2023). Data fusion architecture empowered with deep learning for breast cancer classification. Computers, Materials & Continua, 77(3), 2813-2831. https://doi.org/10.32604/cmc.2023.043013
Vancouver Style
Arooj S, Khan MF, Shahzad T, Khan MA, Nasir MU, Zubair M, et al. Data fusion architecture empowered with deep learning for breast cancer classification. Comput Mater Contin. 2023;77(3):2813-2831 https://doi.org/10.32604/cmc.2023.043013
IEEE Style
S. Arooj et al., "Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification," Comput. Mater. Contin., vol. 77, no. 3, pp. 2813-2831. 2023. https://doi.org/10.32604/cmc.2023.043013



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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