Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment.

Autor: Abd Elaziz M; Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates; MEU Research Unit, Middle East University, Amman 11831, Jordan. Electronic address: abd_el_aziz_m@yahoo.com., Dahou A; Mathematics and Computer Science Department, University of Ahmed DRAIA, 01000, Adrar, Algeria; LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, 01000, Adrar, Algeria. Electronic address: dahou.abdghani@univ-adrar.edu.dz., Aseeri AO; Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia. Electronic address: a.aseeri@psau.edu.sa., Ewees AA; Department of Computer, Damietta University, Damietta 34517, Egypt. Electronic address: ewees@du.edu.eg., Al-Qaness MAA; College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China; Zhejiang Optoelectronics Research Institute, Jinhua 321004, China; College of Engineering and Information Technology, Emirates International University, Sana'a 16881, Yemen. Electronic address: alqaness@zjnu.edu.cn., Ibrahim RA; Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt. Electronic address: rehab100r@yahoo.com.
Jazyk: angličtina
Zdroj: Computational biology and chemistry [Comput Biol Chem] 2024 Aug; Vol. 111, pp. 108110. Date of Electronic Publication: 2024 May 22.
DOI: 10.1016/j.compbiolchem.2024.108110
Abstrakt: The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: