Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images.

Autor: Chauhan S; Department of Computer Science and Engineering, National Institute of Technology Goa, Ponda, 403401, Goa, India., Edla DR; Department of Computer Science and Engineering, National Institute of Technology Goa, Ponda, 403401, Goa, India., Boddu V; Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Hanamkonda, 506004, Telangana, India., Rao MJ; Department of CSE, Aditya Institute of Technology and Management, Kotturu, Tekkali, Andhra Pradesh, India., Cheruku R; Department of Computer Science and Engineering, National Institute of Technology Warangal, Hanamkonda, 506004, Telangana, India., Nayak SR; School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, Odisha, India., Martha S; School of Computer Science and Artificial Intelligence, SR University, Warangal, 506004, Telangana, India., Lavanya K; University College of Sciences, Acharya Nagarjuna Univesity, Guntur, Andhra Pradesh, India., Nigat TD; Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma, Oromia, Ethiopia. tsedidebebe@gmail.com.
Jazyk: angličtina
Zdroj: BMC medical imaging [BMC Med Imaging] 2024 Jan 02; Vol. 24 (1), pp. 1. Date of Electronic Publication: 2024 Jan 02.
DOI: 10.1186/s12880-023-01155-7
Abstrakt: Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vision. The COVID-19 detection is the subject of our medical study. Researchers attempted to increase the detection accuracy but at the cost of high model complexity. In this paper, we desire to achieve better accuracy with little training space and time so that this model easily deployed in edge devices. In this paper, a new CNN design is proposed that has three stages: pre-processing, which removes the black padding on the side initially; convolution, which employs filter banks; and feature extraction, which makes use of deep convolutional layers with skip connections. In order to train the model, chest X-ray images are partitioned into three sets: learning(0.7), validation(0.1), and testing(0.2). The models are then evaluated using the test and training data. The LMNet, CoroNet, CVDNet, and Deep GRU-CNN models are the other four models used in the same experiment. The propose model achieved 99.47% & 98.91% accuracy on training and testing respectively. Additionally, it achieved 97.54%, 98.19%, 99.49%, and 97.86% scores for precision, recall, specificity, and f1-score respectively. The proposed model obtained nearly equivalent accuracy and other similar metrics when compared with other models but greatly reduced the model complexity. Moreover, it is found that proposed model is less prone to over fitting as compared to other models.
(© 2023. The Author(s).)
Databáze: MEDLINE
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