BEVGGC: Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images

Abstract

coronavirus disease has caused more than 2 million deaths worldwide. Mainly diagnostic methods of COVID-19 are: (i) nucleic acid testing. This method requires high requirements on the sample testing environment. When collecting samples, staff are in a susceptible environment, which increases the risk of infection. (ii) chest computed tomography. The cost of it is high and some radiation in the scan process. (iii) chest X-ray images. It has the advantages of fast imaging, higher spatial recognition than chest computed tomography. Therefore, our team chose the chest X-ray images as the experimental dataset in this paper. Methods: We proposed a novel framework-BEVGG and three methods (BEVGGC-I, BEVGGC-II, and BEVGGC-III) to diagnose COVID-19 via chest X-ray images. Besides, we used biogeography-based optimization to optimize the values of hyperparameters of the convolutional neural network. Results: The experimental results show that the OA of our proposed three methods are 97.65% ± 0.65%, 94.49% ± 0.22% and 94.81% ± 0.52%. BEVGGC-I has the best performance of all methods. Conclusions: The OA of BEVGGC-I is 9.59% ± 1.04% higher than that of state-of-the-art methods.

A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network

“…Pham et al [19] proposed a supervised multi-label classification framework based on CNN, which uses the hierarchical dependence between abnormal labels to improve the diagnosis accuracy of chest Xray images. Sun et al [20] proposed a novel framework of BEVGG for diagnosing COVID-19 through chest X-ray images and used a biogeography-based optimization method to optimize hyperparameter values of convolutional neural networks, and the experimental results showed that the framework performs higher than the current methods. Gayathri et al [21] proposed a computer-assisted diagnosis method using chest X-ray images, sparse autoloader, and feedforward neural network FFNN, which reached high results in diagnosing COVID-19.…”

Introduction to the Special Issue on Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph

“…The sixth paper "BEVGGC: Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images" by Sun et al [6]. They proposed a novel framework-BEVGG and three methods (BEVGGC-I, BEVGGC-II, and BEVGGC-III) to diagnose COVID-19 via chest X-ray images.…”