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).