Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach

Abstract

Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the alternative solution of detection is Computed Tomography (CT) images. In this paper, Contrast Limited Histogram Equalization (CLAHE) was applied to CT images as a preprocessing step for enhancing the quality of the images. After that, we developed a novel Convolutional Neural Network (CNN) model that extracted 100 prominent features from a total of 2482 CT scan images. These extracted features were then deployed to various machine learning algorithms - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Finally, we proposed an ensemble model for the COVID19 CT image classification. We also showed various performance comparisons with the state-of-art methods. Our proposed model outperforms the state-of-art models and achieved an accuracy, precision, and recall score of 99.73%, 99.46%, and 100%, respectively.

Keywords: CLAHE; COVID19; Convolutional Neural Network; Ensemble learning; Feature scaling; Guassian Naive Bayes; Soft voting; Support Vector Machine.

Fig. 1

Proposed framework for detection of COVID19.

Fig. 2

(A) Original image, (B) Image after applying CLAHE.

Fig. 3

Feature extracted using CNN model.

Fig. 4

Proposed ensemble learning model.

Fig. 5

CT scan images of: (A) COVID19 infected, (B) Non-COVID19 infected patients.

Fig. 6

Confusion matrices for (A) GNB, (B) SVM, (C) DT, (D) LR, (E) RF, and (F) Ensemble model.

Fig. 7

Receiver Operating Characteristics (ROC) curve for machine learning models.