Work

COVID‐19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm‐Based Feature Selection from X‐Ray Images

Year: 2021

Type: article

Abstract: The new COVID‐19 is rapidly spreading and has already claimed the lives of numerous people. The virus is highly destructive to the human lungs, and early detection is critical. As a result, this paper presents a hybrid approach based on deep convolutional neural networks that are very effective tools for image classification. The feature vectors were extracted from the images using a deep convolutional neural network, and the binary differential metaheuristic algorithm was used to select the most valuable features. The SVM classifier was then given these optimized features. For the study, a database containing images from three categories, including COVID‐19, pneumonia, and a healthy category, included 1092 X‐ray samples, was used. The proposed method achieved a 99.43% accuracy, a 99.16% sensitivity, and a 99.57% specificity. Our findings indicate that the proposed method outperformed recent studies on COVID‐19 detection using X‐ray images.

Source: Complexity

DOI: 10.1155/2021/9973277

Authors: Mohammad Saber Iraji, Mohammad-Reza Feizi-Derakhshi, Jafar Tanha

Institutions: University of Tabriz, Payame Noor University

Language: English

Topic: COVID-19 diagnosis using AI

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,760