An Efficient Approach to Predict COVID-19 Infected Patient Applying Deep learning Algorithm on Chest X-ray Images with Analyzing the Patient Symptoms (Preprint)

2020 ◽  
Author(s):  
Mohammad Helal Uddin ◽  
Mohammad Nahid Hossain ◽  
K. Thapa ◽  
S.-H Yang
Keyword(s):  
Learning Algorithm ◽  
Early Stage ◽  
Low Cost ◽  
Research Work ◽  
Lung Infection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
Life Threatening ◽  
Chest X Ray

BACKGROUND COVID-19 is a life-threatening infectious disease that has become a pandemic for the time being. The virus grows within the lower respiratory tract where early-stage symptoms(like- cough, fever, sore throat, etc.) develop and then it causes lung infection(pneumonia) OBJECTIVE This paper proposed a new methodology of artificial testing whether a patient has been infected by COVID-19 or not METHODS We have presented a prediction model based on, Convolutional Neural Networks(CNN) and our own developed mathematical equation based algorithm named SymptomNet. The CNN algorithm classifies the lung infection(pneumonia) from frontal chest X-ray images, while the symptoms analyzing algorithm(SymptomNet) predicts the possibility of COVID-19 infection from developed symptoms in a patient RESULTS The model has the accuracy of 96% while predicting COVID-19 patients. In this Model, the CNN classifier has the accuracy of around 96% and the SymptomNet algorithm has the accuracy of 97%. CONCLUSIONS This research work obtained a promising accuracy while predicting COVID-19 infected patients. The proposed model can be ubiquitously used at a low cost with high accuracy.

scholarly journals 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

2020 ◽  
Vol 10 (4) ◽  
pp. 213 ◽  
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.

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 Kernel Convolutional Neural Network Deep Learning Algorithm To Classify LIVER Disease

2019 ◽  
Vol 8 (6) ◽  
pp. 2002-2007
Author(s):  
Pardeep Kaur ◽  
Harinder Kaur
Keyword(s):  
Liver Disease ◽  
Learning Algorithm ◽  
Eating Habits ◽  
Kernel Method ◽  
Early Stage ◽  
Research Work ◽  
Disease Diagnosis ◽  
The Body ◽  
Common Disease ◽  
Deep Learning Algorithm

Now a day, liver disease is common disease due to the bad eating habits among individuals. Some disturbance in the functioning of the liver may cause liver sickness. Liver is responsible for overall functioning of the body. Hence, it becomes necessary to diagnosis the liver disease at an early stage. In advanced world of technology, various methods has been been developed to diagnosis and detect the disease includes data mining. This is novel concept to determine the data by extracting features and recognize indications of liver disease by medical experts. The existing technique has implemented optimize the rules released from Boosted classification with a genetic algorithm, to enhance the LDD (Liver Disease Diagnosis) interval of time and accuracy level. Hence, GA is utilized for enhancing and enhancing directions of another method. In this research work, defines a novel method ECNN (Enhanced CNN) of LDD and enable medical specialists to recognize sign of disease and optimization is done for maximum period, decrease the death rate. Clustering and Feature extraction phase to extract the unique feature based on Kernel method and divide the data into a group or cluster-based using FCM algorithm. Implement CNN method to predict or detect the liver disease to improve the performance and classification of rules set. The proposed method has implemented to achieve better performance and compared with existing methods. The simulation tool used in this research works MATLAB 2016a and calculates the performance is Accuracy achieved 96 % ad existing GA accuracy rate 92.9 % achieved in our work

scholarly journals Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

2020 ◽  
Author(s):  
Muntasir Al-Asfoor
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Normal Subjects ◽  
Learning Approach ◽  
Computer Aided Detection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Chest X Ray ◽  
The Times

Abstract During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial Intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest x-ray images to detect the infected subjects. An enhanced Convolutional Neural Network (CNN) architecture has been designed with 22 layers which is then trained over a chest x-ray dataset. More after, a classification component has been introduced to classify the x-ray images into two categories (Covid-19 and not Covid-19) of infection. The system has been evaluated through a series of observations and experimentation. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed Covid-19 with accuracy of 95.7% and normal subjects with accuracy of 93.1 while it showed 96.7 accuracy on Pneumonia.

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

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.

scholarly journals A federated approach for detecting the chest diseases using DenseNet for multi-label classification

2021 ◽  
Author(s):  
K. V. Priya ◽  
J. Dinesh Peter
Keyword(s):  
Deep Neural Networks ◽  
Early Stage ◽  
Disease Classification ◽  
Classification Algorithms ◽  
Classification Problems ◽  
Improve Performance ◽  
X Ray ◽  
Full Dataset ◽  
Proposed Model ◽  
Chest X Ray

AbstractMulti-label disease classification algorithms help to predict various chronic diseases at an early stage. Diverse deep neural networks are applied for multi-label classification problems to foresee multiple mutually non-exclusive classes or diseases. We propose a federated approach for detecting the chest diseases using DenseNets for better accuracy in prediction of various diseases. Images of chest X-ray from the Kaggle repository is used as the dataset in the proposed model. This new model is tested with both sample and full dataset of chest X-ray, and it outperforms existing models in terms of various evaluation metrics. We adopted transfer learning approach along with the pre-trained network from scratch to improve performance. For this, we have integrated DenseNet121 to our framework. DenseNets have a few focal points as they help to overcome vanishing gradient issues, boost up the feature propagation and reuse and also to reduce the number of parameters. Furthermore, gradCAMS are used as visualization methods to visualize the affected parts on chest X-ray. Henceforth, the proposed architecture will help the prediction of various diseases from a single chest X-ray and furthermore direct the doctors and specialists for taking timely decisions.

scholarly journals An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

scholarly journals Diagnosing gastric volvulus in chest X-ray: report of three cases

2020 ◽  
Vol 2020 (7) ◽  
Author(s):  
Narendra Pandit ◽  
Abhijeet Kumar ◽  
Tek Narayan Yadav ◽  
Qamar Alam Irfan ◽  
Sujan Gautam ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Acute Abdomen ◽  
Gastric Volvulus ◽  
X Ray ◽  
Pain Abdomen ◽  
Life Threatening ◽  
Chest X Ray ◽  
High Index Of Suspicion ◽  
Specific Symptoms ◽  
Prompt Diagnosis

Abstract Gastric volvulus is a rare abnormal rotation of the stomach along its axis. It is a surgical emergency, hence requires prompt diagnosis and treatment to prevent life-threatening gangrenous changes. Hence, a high index of suspicion is required in any patients presenting with an acute abdomen in emergency. The entity can present acutely with pain abdomen and vomiting, or as chronic with non-specific symptoms. Chest X-ray findings to diagnose it may be overlooked in patients with acute abdomen. Here, we report three patients with gastric volvulus, where the diagnosis was based on the chest X-ray findings, confirmed with computed tomography, and managed successfully with surgery.

Life-threatening presentation of a parahiatal hernia after esophagectomy: a case report and review of the literature

2021 ◽  
Vol 14 (6) ◽  
pp. e242158
Author(s):  
Camille Plourde ◽  
Émilie Comeau
Keyword(s):  
Small Bowel ◽  
Transverse Colon ◽  
Acute Abdominal Pain ◽  
Haemodynamic Instability ◽  
Acute Setting ◽  
X Ray ◽  
Left Diaphragm ◽  
Life Threatening ◽  
Chest X Ray ◽  
Obstructive Shock

A woman presented to our hospital with acute abdominal pain 7 months following an oesophagectomy. A chest X-ray revealed a new elevation of the left diaphragm. CT demonstrated a large left diaphragmatic hernia incarcerated with non-enhancing transverse colon and loops of small bowel. She deteriorated rapidly into obstructive shock and was urgently brought to the operating room for a laparotomy. The diaphragmatic orifice was identified in a left parahiatal position, consistent with a parahiatal hernia. Incarcerated necrotic transverse colon and ischaemic loops of small bowel were resected, and the diaphragmatic defect was closed primarily. Because of haemodynamic instability, the abdomen was temporarily closed, and a second look was performed 24 hours later, allowing anastomosis and definitive closure. Parahiatal hernias are rare complications following surgical procedures and can lead to devastating life-threatening complications, such as an obstructive shock. Expeditious diagnosis and management are required in the acute setting.

scholarly journals A dangerous pleural effusion

2010 ◽  
Vol 92 (5) ◽  
pp. e53-e54 ◽  
Author(s):  
Somprakas Basu ◽  
Shilpi Bhadani ◽  
Vijay K Shukla
Keyword(s):  
Rare Complication ◽  
Sudden Onset ◽  
Biliary Surgery ◽  
Biliary Leak ◽  
X Ray ◽  
Delay In Diagnosis ◽  
Aged Woman ◽  
Middle Aged Woman ◽  
Life Threatening ◽  
Chest X Ray

Bilothorax is a rare complication of biliary peritonitis and, if not treated promptly, can be life-threatening. We report a case of a middle-aged woman who had undergone a bilio-enteric bypass and subsequently a biliary leak developed, which finally led to intra-abdominal biliary collection and spontaneous bilothorax. The clinical course was rapid and mimicked venous thromboembolism, myocardial infarction and pulmonary oedema, which led to a delay in diagnosis and management and finally death. We high-light the fact that bilothorax, although a rare complication of biliary surgery, should always be considered as a probable cause of massive effusion and sudden-onset respiratory and cardiovascular collapse in the postoperative period. A chest X-ray and a diagnostic pleural tap can confirm the diagnosis. Once detected, an aggressive management should be instituted to prevent organ failure and death.

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