COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

Abstract

The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

“…Another study published in [41] proposed a diagnostic system based on the majority voting method according to the results given by five classifiers: MobileNetV2, ResNet50V2, ResNet50V1, DenseNet201, and ResNet11.…”

Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

“…Remarkable progress has been made in the automated detection of COVID-19 in CXRs [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Several studies in the literature have leveraged deep convolutional neural networks (CNNs) with and without modifications to convincingly predict COVID-19.…”

“…Several multi-classifications of COVID-19 from normal, pneumonia, and TB have been developed using deep CNNs [15][16][17][18][19][20][21]. Wang et al [15] first introduced an opensource COVID-Net to identify COVID-19 CXRs using a customized CNN model.…”

“…Using the dataset consisting of 125 COVID-19, 500 normal, and 500 pneumonia CXRs, the binary classifier achieved 98.08% accuracy, whereas the multi-class classifier achieved 87.02%. Ben Jabra et al [20] compared the performance of 16 pretrained CNN models and combined the best-performing methods as an ensemble classifier to enhance the accuracy. They achieved the highest accuracy of 99% with the ensemble classifier by using majority voting on the dataset of 237 COVID-19, 1338 normal, and 1336 viral pneumonia CXRs.…”

“…There are two types of majority voting methods: hard and soft voting. Figure 14, inspired by [20], depicts ensemble learning using hard and soft majority voting methods. Hard voting makes the final prediction with the largest sum of votes from the models.…”