COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.

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Diagnosing heart failure from chest X-ray images using deep learning

European Heart Journal ◽

10.1093/ehjci/ehaa946.1201 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

T Matsumoto ◽

S Kodera ◽

H Shinohara ◽

A Kiyosue ◽

Y Higashikuni ◽

...

Keyword(s):

Heart Failure ◽

Deep Learning ◽

Data Augmentation ◽

Learning Algorithm ◽

Funding Source ◽

X Rays ◽

X Ray ◽

Cardiothoracic Ratio ◽

Prediction Probability ◽

Chest X Ray

Abstract The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

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Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm

Journal of Personalized Medicine ◽

10.3390/jpm10040213 ◽

2020 ◽

Vol 10 (4) ◽

pp. 213 ◽

Cited By ~ 1

Author(s):

Ki-Sun Lee ◽

Jae Young Kim ◽

Eun-tae Jeon ◽

Won Suk Choi ◽

Nan Hee Kim ◽

...

Keyword(s):

Deep Learning ◽

Learning Strategies ◽

Transfer Learning ◽

Learning Algorithm ◽

Fine Tuning ◽

Deep Convolutional Neural Networks ◽

X Ray ◽

Backbone Network ◽

Deep Learning Algorithm ◽

Chest X Ray

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

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COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

Biology ◽

10.3390/biology10111174 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1174

Author(s):

Shamima Akter ◽

F. M. Javed Mehedi Shamrat ◽

Sovon Chakraborty ◽

Asif Karim ◽

Sami Azam

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Statistical Significance ◽

Confusion Matrix ◽

Original Data ◽

Rank Test ◽

X Rays ◽

X Ray ◽

Professional Exposure ◽

Chest X Ray

COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.

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COVID-19 Data Analysis using Chest X-ray

International Journal of Advanced Medical Sciences and Technology - Regular Issue ◽

10.35940/ijamst.c3018.081421 ◽

2021 ◽

Vol 1 (4) ◽

pp. 5-10

Author(s):

Ishtiaque Ahmed ◽

◽

Manan Darda ◽

Neha Tikyani ◽

Rachit Agrawal ◽

...

Keyword(s):

Deep Learning ◽

Data Analysis ◽

Predictive Value ◽

Large Scale ◽

X Rays ◽

Learning Models ◽

Data Set ◽

X Ray ◽

Dataset Size ◽

Chest X Ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

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A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning

Journal of Healthcare Engineering ◽

10.1155/2021/1002799 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Omar Faruk ◽

Eshan Ahmed ◽

Sakil Ahmed ◽

Anika Tabassum ◽

Tahia Tazin ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Computer Assisted ◽

Promising Technique ◽

Suggested Approach ◽

X Ray ◽

Chest X Ray ◽

Deep Learning Model

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

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Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/8036304 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Surya Krishnamurthy ◽

Kathiravan Srinivasan ◽

Saeed Mian Qaisar ◽

P. M. Durai Raj Vincent ◽

Chuan-Yu Chang

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Large Scale ◽

Data Augmentation ◽

Medical Image Analysis ◽

Predictive Performance ◽

X Ray ◽

Recent Developments ◽

Life Threatening ◽

Chest X Ray

Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.

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CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV

npj Digital Medicine ◽

10.1038/s41746-020-00322-2 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Pranav Rajpurkar ◽

Chloe O’Connell ◽

Amit Schechter ◽

Nishit Asnani ◽

Jason Li ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Clinical Information ◽

Hiv Positive ◽

X Rays ◽

Web Based ◽

X Ray ◽

Deep Learning Algorithm ◽

Learning Assistance ◽

Chest X Ray

Abstract Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower (p < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.

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Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms

Journal of X-Ray Science and Technology ◽

10.3233/xst-200720 ◽

2020 ◽

Vol 28 (5) ◽

pp. 841-850

Author(s):

Saleh Albahli ◽

Waleed Albattah

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Medical Image Analysis ◽

Image Data ◽

Network Models ◽

Learning Models ◽

X Ray ◽

Study Results ◽

Chest X Ray

OBJECTIVE: This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. METHOD: This study applied transfer learning method to develop deep learning models for detecting COVID-19 disease. Three existing state-of-the-art deep learning models namely, Inception ResNetV2, InceptionNetV3 and NASNetLarge, were selected and fine-tuned to automatically detect and diagnose COVID-19 disease using chest X-ray images. A dataset involving 850 images with the confirmed COVID-19 disease, 500 images of community-acquired (non-COVID-19) pneumonia cases and 915 normal chest X-ray images was used in this study. RESULTS: Among the three models, InceptionNetV3 yielded the best performance with accuracy levels of 98.63% and 99.02% with and without using data augmentation in model training, respectively. All the performed networks tend to overfitting (with high training accuracy) when data augmentation is not used, this is due to the limited amount of image data used for training and validation. CONCLUSION: This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.

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Improved COVID-19 detection using data augmentation Deep Convolution GAN and classifier DenseNet.

10.21203/rs.3.rs-235624/v1 ◽

2021 ◽

Author(s):

Debmitra Ghosh

Keyword(s):

Neural Network ◽

Deep Learning ◽

Classification Accuracy ◽

Deep Neural Network ◽

Data Augmentation ◽

Training Data ◽

X Rays ◽

X Ray ◽

Chest X Ray ◽

Synthetic Images

Abstract SARS-CoV-2 or severe acute respiratory syndrome coronavirus 2 is considered to be the cause of Coronavirus (COVID-19) which is a viral disease. The rapid spread of COVID-19 is having a detrimental effect on the global economy and health. A chest X-ray of infected patients can be considered as a crucial step in the battle against COVID-19. On retrospections, it is found that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This sparked the introduction of a variety of deep learning systems and studies which have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Although there are certain shortcomings like deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data but the outbreak is recent, so it is large datasets of radiographic images of the COVID-19 infected patients are not available in such a short time. Here, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing a Deep Convolution Generative Adversarial Network-based model. In addition, we demonstrate that the synthetic images produced from DCGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. Although there are several models available, we chose MobileNet as it is a lightweight deep neural network, with fewer parameters and higher classification accuracy. Here we are using a deep neural network-based model to diagnose COVID-19 infected patients through radiological imaging of 5,859 Chest X-Ray images. We are using a Deep Convolutional Neural Network and a pre-trained model “DenseNet 121” for two new label classes (COVID-19 and Normal). To improve the classification accuracy, in our work we have further reduced the number of network parameters by introducing dense blocks that are proposed in DenseNets into MobileNet. By adding synthetic images produced by DCGAN, the accuracy increased to 97%. Our goal is to use this method to speed up COVID-19 detection and lead to more robust systems of radiology.

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COVID-19 Data Analysis using Chest X-Ray

International Journal of Advanced Medical Sciences and Technology - Regular Issue ◽

10.54105/ijamst.c3018.081421 ◽

2021 ◽

pp. 5-10

Author(s):

Ishtiaque Ahmed ◽

◽

Manan Darda ◽

Neha Tikyani ◽

Rachit Agrawal ◽

...

Keyword(s):

Deep Learning ◽

Data Analysis ◽

Predictive Value ◽

Large Scale ◽

X Rays ◽

Learning Models ◽

Data Set ◽

X Ray ◽

Dataset Size ◽

Chest X Ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID-19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

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