MobNas ensembled model for breast cancer prediction.
Authors
Affiliations (11)
Affiliations (11)
- Department of Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan.
- Department of Computing and Information Technology, Gomal University, D.I.Khan, 29050, Pakistan.
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan. [email protected].
- Department of Computer Science, School Education Department, Government of Punjab, Layyah, 31200, Pakistan. [email protected].
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia.
- Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, 12555, United Arab Emirates.
- Department of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences (UMS), Sanaa, 00967, Yemen. [email protected].
- Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada.
- School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa.
- International Institute of Technology and Management (IITG), Av. Grandes Ecoles, BP 1989, Libreville, Gabon.
- College of Computer Science and Eng. (Invited Prof.), University of Ha'il, Ha'il, 55476, Saudi Arabia.
Abstract
Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning algorithms, deep learning, and transfer learning models, issues with diagnostic accuracy and minimizing diagnostic errors persist. This paper introduces MobNAS, a model that uses MobileNetV2 and NASNetLarge to sort breast cancer images into benign, malignant, or normal classes. The study employs a multi-class classification design and uses a publicly available dataset comprising 1,578 ultrasound images, including 891 benign, 421 malignant, and 266 normal cases. By deploying MobileNetV2, it is easy to work well on devices with less computational capability than is used by NASNetLarge, which enhances its applicability and effectiveness in other tasks. The performance of the proposed MobNAS model was tested on the breast cancer image dataset, and the accuracy level achieved was 97%, the Mean Absolute Error (MAE) was 0.05, and the Matthews Correlation Coefficient (MCC) was 95%. From the findings of this research, it is evident that MobNAS can enhance diagnostic accuracy and reduce existing shortcomings in breast cancer detection.