A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Abstract

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.

Keywords: Convolutional neural network; Coronavirus; Deep learning; Pneumonia; Rediology; Respiratory diseases.

Fig. 1

Ground Glass Opacity was identified in the left middle to lower lung opacity (shown by a white arrow inside the red circle) [33]. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Block Diagram of Proposed methodology.

Fig. 3

Illustration of different filters a. Generated mask b. Variant of Laplacian filter c. Sharpening filter.

Fig. 4

Prepossessing operations on Chest CT-scan & X-ray images.

Fig. 5

Samples of augmented images of Chest CT-scan & X-ray.

Fig. 6

The overview of Residual connection.

Fig. 7

Proposed architecture.

Fig. 8

Flattening operation..

Fig. 9

A sample of dropout operation.

Fig. 10

Sample images from the collected dataset a. Chest X-ray images of different types of cases of interest: COVID-19, normal (control), pneumonia with bacterial infection and Pneumonia with viral infection. b. Chest CT-scan images: COVID-19, normal (control) and community acquired pneumonia cases.

Fig. 11

Schematic illustration of five -fold cross validation approach.

Fig. 12

Classification performance results of 4-class using fold-3 chest x-ray dataset.

Fig. 13

Classification performance results on 3-class using fold-3 chest x-ray datasets.

Fig. 14

Classification performance results on 2-class using fold-2 chest x-ray dataset.

Fig. 15

Average precision, recall & f1-score of 4-class, 3-class (a. covid vs pneu_bac vs normal, b. covid vs pneu_vir vs normal), 2-class (a. covid vs pneu_vir, b. covid vs normal, c. covid vs pneu_bac).

Fig. 16

a. Cross folding accuracy on each fold considering 4-class, 3-class (covid vs pneu_bac vs normal), 2-class (covid vs pneu_vir) and b. Average accuracy on the considered class.

Fig. 17

Classification performance results on 3-class using fold-1 chest ct-scan dataset.

Fig. 18

a. Cross folding accuracy on each fold considering 3-class (covid vs cap vs normal) b. Average precision, recall & f1-score of 3-class (covid vs cap vs normal), 2-class (a.covid vs cap, b.covid vs normal).