Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images

Abstract

Background: The acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease seriously affected worldwide health. It remains an important worldwide concern as the number of patients infected with this virus and the death rate is increasing rapidly. Early diagnosis is very important to hinder the spread of the coronavirus. Therefore, this article is intended to facilitate radiologists automatically determine COVID-19 early on X-ray images. Iterative Neighborhood Component Analysis (INCA) and Iterative ReliefF (IRF) feature selection methods are applied to increase the accuracy of the performance criteria of trained deep Convolutional Neural Networks (CNN). Materials and methods: The COVID-19 dataset consists of a total of 15153 X-ray images for 4961 patient cases. The work includes thirteen different deep CNN model architectures. Normalized data of lung X-ray image for each deep CNN mesh model are analyzed to classify disease status in the category of Normal, Viral Pneumonia and COVID-19. The performance criteria are improved by applying the INCA and IRF feature selection methods to the trained CNN in order to improve the analysis, forecasting results, make a faster and more accurate decision. Results: Thirteen different deep CNN experiments and evaluations are successfully performed based on 80-20% of lung X-ray images for training and testing, respectively. The highest predictive values are seen in the analysis using INCA feature selection in the VGG16 network. The means of performance criteria obtained using the accuracy, sensitivity, F-score, precision, MCC, dice, Jaccard, and specificity are 99.14%, 97.98%, 99.58%, 98.80%, 97.81%, 98.83%, 97.68%, and 99.56%, respectively. This proposed study is indicated the useful application of deep CNN models to classify COVID-19 in X-ray images.