Automatic Diagnosis of Covid-19 Using Chest X-ray Images Through Deep Learning Models

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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Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

Journal of Healthcare Engineering ◽

10.1155/2021/8829829 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Manjit Kaur ◽

Vijay Kumar ◽

Vaishali Yadav ◽

Dilbag Singh ◽

Naresh Kumar ◽

...

Keyword(s):

Deep Learning ◽

Learning Models ◽

Huge Number ◽

X Ray ◽

Screening Model ◽

Proposed Model ◽

Strength Pareto Evolutionary Algorithm ◽

Chest X Ray ◽

Deep Learning Model

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

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Lightweight deep learning models for detecting COVID-19 from chest X-ray images

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2020.104181 ◽

2021 ◽

Vol 130 ◽

pp. 104181

Author(s):

Stefanos Karakanis ◽

Georgios Leontidis

Keyword(s):

Deep Learning ◽

Learning Models ◽

X Ray ◽

Chest X Ray

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Optimal Hyperparameter Selection of Deep Learning Models for COVID-19 Chest X-ray Classification

Intelligence-Based Medicine ◽

10.1016/j.ibmed.2021.100034 ◽

2021 ◽

pp. 100034

Author(s):

Adeyinka P. Adedigba ◽

Steve A. Adeshina ◽

Oluwatomisin E. Aina ◽

Abiodun M. Aibinu

Keyword(s):

Deep Learning ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Hyperparameter Selection ◽

Selection Of

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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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COVID-19 detection in Chest X-ray Images using Deep Learning

10.21203/rs.3.rs-65954/v1 ◽

2020 ◽

Author(s):

Ebru Erdem ◽

Tolga Aydın

Keyword(s):

Deep Learning ◽

Deep Neural Networks ◽

Specific Information ◽

The Novel ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Lung Infections ◽

Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All analyzes were performed on CXR data received on the publicly available. This paper offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of disease. Empirical evaluations demonstrate that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models.

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