A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images

Abstract

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).

References in corpus (11)

• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
• COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
• Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
• Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
• Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
• An Incentive Based Approach for COVID-19 using Blockchain Technology
• Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review

Cited by in corpus (2)

• Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
• Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM