scholarly journals Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 20235-20254
Author(s):  
Hanan S. Alghamdi ◽  
Ghada Amoudi ◽  
Salma Elhag ◽  
Kawther Saeedi ◽  
Jomanah Nasser
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

scholarly journals An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Okeke Stephen ◽  
Mangal Sain ◽  
Uchenna Joseph Maduh ◽  
Do-Un Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Classification Performance ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

scholarly journals A Review on Deep Learning Approaches for COVID-19 Detection in Chest X-Ray Images

2021 ◽  
Vol 9 (10) ◽  
pp. 1792-1801
Author(s):  
Tanishka Dodiya
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
High Rate ◽  
Learning Approaches ◽  
X Ray ◽  
First Case ◽  
X Ray Imaging ◽  
The World ◽  
Chest X Ray ◽  
Diagnostic Capabilities

Abstract: COVID-19 also famously known as Coronavirus is one of the deadliest viruses found in the world, which has a high rate in both demise and spread. This has caused a severe pandemic in the world. The virus was first reported in Wuhan, China, registering causes like pneumonia. The first case was encountered on December 31, 2019. As of 20th October 2021, more than 242 million cases have been reported in more than 188 countries, and it has around 5 million deaths. COVID- 19 infected persons have pneumonia-like symptoms, and the infection damages the body's respiratory organs, making breathing difficult. The elemental clinical equipment as of now being employed for the analysis of COVID-19 is RT-PCR, which is costly, touchy, and requires specific clinical workforce. According to recent studies, chest X-ray scans include important information about the start of the infection, and this information may be examined so that diagnosis and treatment can begin sooner. This is where artificial intelligence meets the diagnostic capabilities of intimate clinicians. X-ray imaging is an effectively available apparatus that can be an astounding option in the COVID-19 diagnosis. The architecture usually used are VGG16, ResNet50, DenseNet121, Xception, ResNet18, etc. This deep learning based COVID detection system can be installed in hospitals for early diagnosis, or it can be used as a second opinion. Keywords: COVID-19, Deep Learning, CNN, CT-Image, Transfer Learning, VGG, ResNet, DenseNet

scholarly journals Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

2021 ◽  
Vol 11 (10) ◽  
pp. 993
Author(s):  
Roberta Fusco ◽  
Roberta Grassi ◽  
Vincenza Granata ◽  
Sergio Venanzio Setola ◽  
Francesca Grassi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Standard Deviation ◽  
Mean Value ◽  
Learning Approaches ◽  
Inclusion Criteria ◽  
X Ray ◽  
Standard Deviation Range ◽  
Chest X Ray

Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. Methods: Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). Results: Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4–99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0–100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0–99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0–100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). Conclusions: Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.

Deep Learning Approaches for Detecting COVID-19 from Chest X-ray Images: A Survey (Preprint)

2020 ◽  
Author(s):  
Hanan Alghamdi ◽  
Ghada Amoudi ◽  
Salma Elhag ◽  
Kawther Saeedi ◽  
Jomanah Nasser
Keyword(s):  
Deep Learning ◽  
Visual Information ◽  
Low Cost ◽  
Limited Resource ◽  
Learning Approaches ◽  
X Ray ◽  
Wide Range ◽  
Control And Prevention ◽  
Chest X Ray ◽  
Learning Architectures

UNSTRUCTURED Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions

scholarly journals Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ivo M. Baltruschat ◽  
Hannes Nickisch ◽  
Michael Grass ◽  
Tobias Knopp ◽  
Axel Saalbach
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

Deep Learning Approaches for Detection of Covid-19 Using Chest X-Ray Images

2021 ◽  
Author(s):  
Hridya Krishna R ◽  
Vaishnavi K P ◽  
M Anagha Ramadas ◽  
Nikita Chanalya ◽  
Anjali Manoj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

scholarly journals Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches

2020 ◽  
Author(s):  
Khair Ahammed ◽  
Md. Shahriare Satu ◽  
Mohammad Zoynul Abedin ◽  
Md. Auhidur Rahaman ◽  
Sheikh Mohammed Shariful Islam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Sampling ◽  
Learning Approaches ◽  
X Ray ◽  
Learning Procedure ◽  
Community Transmission ◽  
Chest X Ray ◽  
Single Dataset ◽  
F Measure

AbstractThis study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy (94.03%), AUC (95.52%), f-measure (94.03%), sensitivity (94.03%) and specificity (97.01%) as well as the lowest fall out (4.48%) and miss rate (2.98%) respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.

scholarly journals Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
M. D. Kamrul Hasan ◽  
Sakil Ahmed ◽  
Z. M. Ekram Abdullah ◽  
Mohammad Monirujjaman Khan ◽  
Divya Anand ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
Learning Tools ◽  
Human Beings ◽  
X Ray ◽  
Detection Model ◽  
Medical Issues ◽  
Average Accuracy ◽  
History Of ◽  
Chest X Ray

The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python’s to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy.

scholarly journals Validating deep learning inference during chest X-ray classification for COVID-19 screening

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robbie Sadre ◽  
Baskaran Sundaram ◽  
Sharmila Majumdar ◽  
Daniela Ushizima
Keyword(s):  
Deep Learning ◽  
Heterogeneous Data ◽  
Classification Performance ◽  
Public Image ◽  
Learning Approaches ◽  
X Ray ◽  
New Methods ◽  
Heterogeneous Data Sources ◽  
Chest X Ray ◽  
Lung Screening

AbstractThe new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

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