scholarly journals MCNN: A Deep Learning Based Rapid Diagnosis Method for COVID-19 from the X-ray Images

2020 ◽  
Vol 34 (6) ◽  
pp. 673-682
Author(s):  
Ashish Tripathi ◽  
Arush Jain ◽  
Krishna K. Mishra ◽  
Anand Bhushan Pandey ◽  
Prem Chand Vashist
Keyword(s):  
Deep Learning ◽  
Global Economy ◽  
Early Stage ◽  
Binary Classification ◽  
The Novel ◽  
Statistical Parameters ◽  
X Ray ◽  
Diagnosis Method ◽  
Chest X Ray ◽  
Polymerase Chain

Due to the rapidly spreading nature of coronavirus, a pandemic situation has emerged around the world. It is affecting society at large that includes the global economy and public health too. It was found in recent studies that the novel and unknown nature of this virus makes it more difficult to identify and treat the affected patient in the early stage. In this context, a time-consuming method named reverse transcription-polymerase chain reaction (RT-PCR) is being used to detect the positive cases of COVID-19, which requires blood samples of the suspects to diagnose the disease. This paper presents a new deep learning-based method to detect COVID-19 cases using chest X-ray images as the recent studies show that the radiology images have relevant features that can be used to predict the COVID-19. The proposed method is developed for binary classification to identify that a person is infected with COVID-19 or not. A total of 2400 X-ray images are taken for the experimental work. It includes 1000, COVID-19, and 1000, non-COVID-19 images, 200, COVID-19, and 200, non-COVID-19 testing images. The proposed method has been compared with the existing state-of-the-art methods on various statistical parameters which give better results with higher accuracy in diagnosing the COVID-19 cases. The proposed method has obtained 98.25% accuracy, 98.49% precision, 98% sensitivity, 98.50% specificity, and 98.25% F1 score.

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

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.

scholarly journals Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

scholarly journals Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽  
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.

scholarly journals The use of lung ultrasound during the COVID-19 pandemic

2020 ◽  
Vol 27 (SP1) ◽  
pp. e64-e75
Author(s):  
Aly Youssef ◽  
Marta Cavalera ◽  
Carlotta Azzarone ◽  
Carla Serra ◽  
Elena Brunelli ◽  
...  
Keyword(s):  
Health System ◽  
Pulmonary Infection ◽  
Lung Ultrasound ◽  
The Novel ◽  
Chain Reaction ◽  
X Ray ◽  
Rapid Learning ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Novel Coronavirus

The novel coronavirus disease (COVID-19) is a challenge to every health system. Unfortunately, it is unlikely that this pandemic will disappear soon. No health system, with its present resources and workflow, is capable enough to deal with a full-blown wave of this pandemic. Acquisition of specific new skills may be fundamental in delivering appropriate health care for our patients. The gold standard for diagnosis of the COVID-19 infection is real-time reverse transcription polymerase chain reaction. Radiological investigations (chest X-ray or high-resolution computerized tomography [CT]) can be helpful both for diagnosis and management, but they have many limitations. Ultrasound has been suggested as a reliable and accurate tool for assessing the lungs in COVID-19 patients. Lung ultrasound (LUS) can show specific signs of inter-stitial pneumonia, which is characteristic of COVID-19 pulmonary infection. In addition, nonradiologist specialists with experience in ultrasound can be trained on LUS with a relatively rapid learning curve. In pregnancy, LUS can be particularly useful due to the avoidance of exposure to ionizing radiation. In this review, we present the advantages, techniques, and limitations of the use of LUS during the COVID-19 pandemic, with specific focus on pregnancy.

scholarly journals A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

2021 ◽  
Vol 11 (1) ◽  
pp. 844
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Early Stage ◽  
Diagnostic Technique ◽  
Disease Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

scholarly journals Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks

2020 ◽  
Author(s):  
Mundher Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Classification Model ◽  
Statistical Parameters ◽  
Data Set ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractNovel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images.Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was collected from the available X-ray images on public medical repositories. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PyCM* was used to support the statistical parameters. The study revealed the superiority of Model VGG16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

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

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.

scholarly journals Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images

2020 ◽  
Author(s):  
Mugahed A. Al-antari ◽  
Cam-Hao Hua ◽  
Sungyoung Lee
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Lung Diseases ◽  
Diagnostic Performance ◽  
The Novel ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Chest X Ray ◽  
Aided Diagnosis

Abstract Background and Objective: The novel coronavirus 2019 (COVID-19) is a harmful lung disease that rapidly attacks people worldwide. At the end of 2019, COVID-19 was discovered as mysterious lung disease in Wuhan, Hubei province of China. World health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 from the entire digital X-ray images is the key to efficiently assist patients and physicians for a fast and accurate diagnosis.Methods: In this paper, a deep learning computer-aided diagnosis (CAD) based on the YOLO predictor is proposed to simultaneously detect and diagnose COVID-19 among the other eight lung diseases: Atelectasis, Infiltration, Pneumothorax, Mass, Effusion, Pneumonia, Cardiomegaly, and Nodule. The proposed CAD system is assessed via five-fold tests for multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system is trained using an annotated training set of 50,490 chest X-ray images.Results: The suspicious regions of COVID-19 from the entire X-ray images are simultaneously detected and classified end-to-end via the proposed CAD predictor achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. The most testing images of COVID-19 and other lunge diseases are correctly predicted achieving intersection over union (IoU) with their GTs greater than 90%. Applying deep learning regularizers of data balancing and augmentation improve the diagnostic performance by 6.64% and 12.17% in terms of overall accuracy and F1-score, respectively. Meanwhile, the proposed CAD system presents its feasibility to diagnose the individual chest X-ray image within 0.009 second. Thus, the presented CAD system could predict 108 frames/second (FPS) at the real-time of prediction.Conclusion: The proposed deep learning CAD system shows its capability and reliability to achieve promising COVID-19 diagnostic performance among all other lung diseases. The proposed deep learning model seems reliable to assist health care systems, patients, and physicians in their practical validations.

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

2020 ◽  
Author(s):  
Ebru Erdem ◽  
Tolga Aydın
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
Empirical Evaluation ◽  
Specific Information ◽  
The Novel ◽  
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 the analyses were performed on a publicly available CXR dataset. This study 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 a disease. Empirical evaluation demonstrates 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. In addition to the studies in the literature, it has been observed that the proposed pre-trained-based concatenated model gives very similar successful results to the other models.

scholarly journals DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Ahmed Mohamed ◽  
Ahmed Abdelhady
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Open Data ◽  
Disease Outbreak ◽  
Imaging Features ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Data Source

The Coronavirus disease outbreak result in many people to have severe respira- tory problems and it was recognized as a global health threat. Since the virus is targeting the lungs in the human body initially, chest x-ray imaging features were considered to be useful for the detection of the infection in the early stage. In this study, the chest x-ray data of 130 infected patients from an open data source that referenced Cohen J. Morrison P. Dao L., 2020 was used to build a CNN( Convolutional Neural-Network) model for the early detection of the disease. The model was trained with both infected and not-infected peoples’ chest x-ray images with 100 epochs which led to 0.98 accuracy finally. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy.

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