EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers

Abstract

Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In this paper, an automated detection scheme named EMCNet was proposed to identify COVID-19 patients by evaluating chest X-ray images. A convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19. With the extracted features, binary machine learning classifiers (random forest, support vector machine, decision tree, and AdaBoost) were developed for the detection of COVID-19. Finally, these classifiers’ outputs were combined to develop an ensemble of classifiers, which ensures better results for the dataset of various sizes and resolutions. In comparison with other recent deep learning-based systems, EMCNet showed better performance with 98.91% accuracy, 100% precision, 97.82% recall, and 98.89% F1-score. The system could maintain its great importance on the automatic detection of COVID-19 through instant detection and low false negative rate.

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

“…Islam et al 19 employed a CNN for feature extraction and long short-term memory for the classification of patients based on X-ray images. EMCNet 20 is another hybrid diagnosis approach that uses a CNN for feature extraction and carries out binary classification using a number of learning techniques, including RF and support vector machine (SVM), on X-ray images. Islam et al 21 also used a CNN for feature extraction but relied on a recurrent neural network (RNN) for classification based on the extracted features.…”

Deep learning applied to automatic disease detection using chest X‐rays

“…Ensembles were explored later in the pandemic, 75,76 including different binary ML classifiers applied to CNN extracted features 77 and multi‑layer perceptron stacked ensembling 78 …”

A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset

“…In [35] , CT scans have been used to segment infections caused by the new coronavirus. These papers [24] , [25] , [5] , [2] , [29] also worked on classifying CT Scan and X-ray images using machine learning techniques and deep convolutional models.…”