Abstract
The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.
Highlights
The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies
We propose a new approach for detecting COVID-19 infection on chest X-ray images using a decision tree-based ensemble model consisting of two mixtures of discriminative experts (MoE) called EpistoNet
The dataset utilized in this research is comprised of 2500 X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 images provided by Henry Ford Health System (HFHS) of Michigan in Detroit
Summary
Many researches have been recently proposed methods to detect COVID-19 positive cases from CXR and CT imaging using artificial intelligence (AI) and machine learning (ML) techniques. In order to identify COVID-19 from normal or other pneumonia cases, Horry et al.[18] proposed a multimodal classification network based on optimized VGG19 architecture Before training their model, they applied histogram equalization to images followed by enhancement to textures and contrasts using OpenCV library. Authors used pretrained deep CNN models (ResNet[18], ResNet[50], ResNet[101], VGG16, and VGG19) for feature extraction, and the Support Vector Machines (SVM) for classification Their dataset contained 180 COVID-19 and 200 normal chest X-ray images. The deep features extracted from the ResNet[50] model and SVM classifier achieved an accuracy of 94.7% These approaches lack the generalizability for unseen data due to various pre-processing steps performed and assumptions involved in the model development and hyper-parameter fine tuning conducted specific to their own dataset. In this paper we describe the development and evaluation of a new approach for detection of COVID-19 from chest X-ray images using a minimal pre-processing pipeline and automatic optimization of the hyper-parameters of various models using a recently proposed algorithm
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