Details

Autor(en) / Beteiligte

• Chauhan, Sohamkumar
• Edla, Damoder Reddy
• Boddu, Vijayasree
• Rao, M Jayanthi
• Cheruku, Ramalingaswamy
• Nayak, Soumya Ranjan
• Martha, Sheshikala
• Lavanya, Kamppa
• Nigat, Tsedenya Debebe

Titel

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

Ist Teil von

• BMC medical imaging, 2024-01, Vol.24 (1), p.1-1, Article 1

Ort / Verlag

England: BioMed Central

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.