A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images

Abstract

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people’s everyday lives. As the number of COVID-19 cases is rapidly increasing, 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 Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method’s precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.

Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images

“…In other words, measuring one factor, such as precision, is not enough to evaluate the suggested model; hence, we determined the other two factors, recall and F1-score, as listed in Table 2. The results of our methodology are considered improved when compared to the work of Nasiri and Alavi (2022), since they used DenseNet169 that had already been pretrained and consisted of layers, while our network is more compact. However, Nasiri and Alavi (2022) achieved an accuracy of 98.72%, whereas multi-class classification using COVID-19, no findings and pneumonia achieved 92.8%.…”

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

“…Extreme Gradient Boosting (XGBoost), proposed by Chen and Guestrin 49 , is an efficient and scalable ensemble algorithm based on gradient boosted trees 16 , 50 . XGBoost has been used in a wide range of engineering fields, resulting in outstanding performance due to the advantages of parallel tree boosting and using various regularization techniques 13 , 51 , 52 . XGBoost is a stable algorithm with low bias and variance, handling outliers 24 , 53 .…”

Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM

“…The ANOVA method is a popular statistical approach for comparing several independent means. In this method, the features are scored based on the ratio of the between-group variance to the within-group variance through the F-test [33]. The statistical ratio of the F-test is given in (3).…”

“…TN TP FN FP (7) F1-Score: Mathematically speaking, it is the result of dividing the doubled precision and sensitivity by their sum as shown in (8) [33]. Tables III and Table IV represent the comparison of the proposed method with other methods in two-class and multiclass problems, respectively.…”