Affiliation:
1. School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
2. School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu 611730, China
Abstract
The Internet of Medical Things (IoMT) can automate breast tumor detection and classification with the potential of artificial intelligence. However, the leakage of sensitive data can cause harm to patients. To address this issue, this study proposed an intrauterine breast cancer diagnosis method, namely “Privacy-Embedded Lightweight and Efficient Automated (PLA)”, for IoMT, which represents an approach that combines privacy-preserving techniques, efficiency, and automation to achieve our goals. Firstly, our model is designed to achieve lightweight classification prediction and global information processing of breast cancer by utilizing an advanced IoMT-friendly ViT backbone. Secondly, PLA protects patients’ privacy by federated learning, taking the classification task of breast cancer as the main task and introducing the texture analysis task of breast cancer images as the auxiliary task to train the model. For our PLA framework, the classification accuracy is 0.953, the recall rate is 0.998 for the best, the F1 value is 0.969, the precision value is 0.988, and the classification time is 61.9 ms. The experimental results show that the PLA model performs better than all of the comparison methods in terms of accuracy, with an improvement of more than 0.5%. Furthermore, our proposed model demonstrates significant advantages over the comparison methods regarding time and memory.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference55 articles.
1. Beyond self-attention: External attention using two linear layers for visual tasks;Guo;IEEE Trans. Pattern Anal. Mach. Intell.,2022
2. Breast cancer prediction using gated attentive multimodal deep learning;Kayikci;J. Big Data,2023
3. Hamedani-KarAzmoudehFar, F., Tavakkoli-Moghaddam, R., Tajally, A.R., and Aria, S.S. (2023). Breast cancer classification by a new approach to assessing deep neural network-based uncertainty quantification methods. Biomed. Signal Process. Control., 79.
4. Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer;Chakravarthy;IRBM,2023
5. Deep learning-based automatic diagnosis of breast cancer on MRI using mask R-CNN for detection followed by ResNet50 for classification;Zhang;Acad. Radiol.,2023