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

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%.

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Comparison of Classification Models Based on Deep learning on COVID-19 Chest X-Rays

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012078 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012078

Author(s):

Pallavi R Mane ◽

Rajat Shenoy ◽

Ghanashyama Prabhu

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

State Of The Art ◽

Cost Effective ◽

X Rays ◽

The Novel ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Fine Tune

Abstract COVID -19, is a deadly, dangerous and contagious disease caused by the novel corona virus. It is very important to detect COVID-19 infection accurately as quickly as possible to avoid the spreading. Deep learning methods can significantly improve the efficiency and accuracy of reading Chest X-Rays (CXRs). The existing Deep learning models with further fine tune provide cost effective, rapid, and better classification results. This paper tries to deploy well studied AI tools with modification on X-ray images to classify COVID 19. This research performs five experiments to classify COVID-19 CXRs from Normal and Viral Pneumonia CXRs using Convolutional Neural Networks (CNN). Four experiments were performed on state-of-the-art pre-trained models using transfer learning and one experiment was performed using a CNN designed from scratch. Dataset used for the experiments consists of chest X-Ray images from the Kaggle dataset and other publicly accessible sources. The data was split into three parts while 90% retained for training the models, 5% each was used in validation and testing of the constructed models. The four transfer learning models used were Inception, Xception, ResNet, and VGG19, that resulted in the test accuracies of 93.07%, 94.8%, 67.5%, and 91.1% respectively and our CNN model resulted in 94.6%.

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Accessory pathway analysis using a multimodal deep learning model

Scientific Reports ◽

10.1038/s41598-021-87631-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Makoto Nishimori ◽

Kunihiko Kiuchi ◽

Kunihiro Nishimura ◽

Kengo Kusano ◽

Akihiro Yoshida ◽

...

Keyword(s):

Deep Learning ◽

Electrophysiological Study ◽

Accessory Pathway ◽

Learning Model ◽

X Rays ◽

X Ray ◽

Wpw Syndrome ◽

Chest X Ray ◽

Deep Learning Model ◽

Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽

10.3390/covid1010034 ◽

2021 ◽

Vol 1 (1) ◽

pp. 403-415

Author(s):

Abeer Badawi ◽

Khalid Elgazzar

Keyword(s):

Deep Learning ◽

Binary Classification ◽

Healthcare Providers ◽

Large Set ◽

X Rays ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Multi Class Classification ◽

Novel Coronavirus

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

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An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

Applied Sciences ◽

10.3390/app112110301 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10301

Author(s):

Muhammad Shoaib Farooq ◽

Attique Ur Rehman ◽

Muhammad Idrees ◽

Muhammad Ahsan Raza ◽

Jehad Ali ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real World ◽

Early Stage ◽

Binary Classification ◽

X Rays ◽

X Ray ◽

Proposed Model ◽

Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

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COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays

10.21203/rs.3.rs-61891/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rajeev Kumar Singh ◽

Rohan Pandey ◽

Rishie Nandhan Babu

Keyword(s):

Deep Learning ◽

Model Performance ◽

Alternative Methods ◽

Model Complexity ◽

X Rays ◽

Kappa Score ◽

X Ray ◽

Pruning Strategy ◽

Chest X Ray ◽

To Come

Abstract COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delay in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel Deep Learning based solution to rapidly classify COVID -19 patient using chest X-Ray. The proposed solution uses image enhancement, image segmentation and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify Chest X-Ray into three classes viz. COVID-19, Pneumonia and Normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalisability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualisation in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state of the art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-Rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67\% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, Normal, and Pneumonia classes respectively on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold standard clinical and laboratory testing.

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Deep Learning for Screening COVID-19 using Chest X-Ray Images

10.1101/2020.05.04.20090423 ◽

2020 ◽

Cited By ~ 3

Author(s):

Sanhita Basu ◽

Sushmita Mitra ◽

Nilanjan Saha

Keyword(s):

Deep Learning ◽

Lung Parenchyma ◽

Clinical Findings ◽

Data Sets ◽

X Rays ◽

X Ray ◽

Chest X Ray ◽

Increasing Demand ◽

Novel Coronavirus ◽

Diverse Data

AbstractWith the ever increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz. normal, other_disease, pneumonia and Covid — 19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 95.3% ± 0.02. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.

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A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽

10.3390/ai1030027 ◽

2020 ◽

Vol 1 (3) ◽

pp. 418-435

Author(s):

Khandaker Haque ◽

Ahmed Abdelgawad

Keyword(s):

Deep Learning ◽

World Health ◽

X Rays ◽

X Ray ◽

Global Pandemic ◽

Proposed Model ◽

Dental Diseases ◽

Chest X Ray ◽

Artery Disease ◽

Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

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Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning

10.1101/2020.04.11.20054643 ◽

2020 ◽

Cited By ~ 4

Author(s):

Arshia Rehman ◽

Saeeda Naz ◽

Ahmed Khan ◽

Ahmad Zaib ◽

Imran Razzak

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Bacterial Pneumonia ◽

State Of The Art ◽

High Accuracy ◽

X Rays ◽

Early Screening ◽

X Ray ◽

Learning Techniques ◽

The World

AbstractBackgroundCoronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world.AimThe aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques.MethodIn this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures.ResultsEvaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole.ConclusionQuantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.

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IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

10.1101/2020.05.05.20092346 ◽

2020 ◽

Author(s):

Juliana C. Gomes ◽

Valter A. de F. Barbosa ◽

Maira A. Santana ◽

Jonathan Bandeira ◽

Mêuser Jorge Silva Valença ◽

...

Keyword(s):

Intelligent System ◽

Low Cost ◽

Computational Cost ◽

Specific Treatment ◽

Support Vector ◽

X Rays ◽

X Ray ◽

Average Accuracy ◽

Vector Machines ◽

Chest X Ray

AbstractIn late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall and sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.

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Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Sensors ◽

10.3390/s21196655 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6655

Author(s):

Michael Horry ◽

Subrata Chakraborty ◽

Biswajeet Pradhan ◽

Manoranjan Paul ◽

Douglas Gomes ◽

...

Keyword(s):

Lung Cancer ◽

Computer Vision ◽

Deep Learning ◽

Decision Tree ◽

Decision Trees ◽

Lung Nodules ◽

X Rays ◽

Learning Models ◽

X Ray ◽

Chest X Ray

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

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