COVID-19 Dashboard

CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection.

The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models. 

Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays

Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases.

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

/pdf/applications-of-artificial-intelligence-in-battling-against-18cfgukc2t.pdf

Applications of artificial intelligence in battling against covid-19: A literature review.

The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID-19 pneumonia patients using digital chest x-ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID-19, 1345 viral pneumonia and 1341 normal chest xray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.

/pdf/classification-of-covid-19-from-chest-x-ray-images-using-50n2kbunnc.pdf

Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network

Coronavirus disease (COVID‐19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID‐19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID‐19 can protect people from severe infection. Unfortunately, COVID‐19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID‐19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID‐19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID‐19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID‐19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre‐trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X‐rays (1200 cases of COVID‐19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X‐ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID‐19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1‐score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID‐19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.

Detection of COVID‐19 from chest X‐ray images: Boosting the performance with convolutional neural network and transfer learning

Heart disease is a major health issue causing public concern worldwide. Heart disease cases are increasing at a rapid rate every day, so it is very important to predict any such diseases in advance. Therefore, the early diagnosis and prediction of heart disease play a vital role in the correct treatment of patients. In this research article, we have proposed a novel ensemble method using majority voting scheme. First, we compared the performance of different state-of-the-art machine learning classification algorithms for the prediction of heart disease. Six algorithms named as K-nearest neighbor (KNN), Random forest, Naive bayes, Support vector machine (SVM), XGBoost (XGB) and logistic regression were applied and a comparative study was drawn. Several evaluation techniques were used to evaluate the performance of each algorithm using the Cleveland dataset of the UCI repository of heart patients. We proposed a new ensemble classification model by choosing three algorithms based on the best performance. The proposed ensemble approach yields the highest accuracy, precision, recall and F1 score with 92%, 91.1%, 94%, and 93% respectively on the UCI heart disease dataset. Statistical results demonstrate that the robustness of the ensemble method accurately and reliably distinguishes between heart disease and healthy patients.

An Ensemble Machine Learning Method for the Prediction of Heart Disease

Kidney abnormalities are common in the world and due to the scarcity of urologists, there is a need to develop an automated system for the detection of kidney diseases. In this paper, we present a powerful and unique deep transfer learning (TL) architecture based on a pre-trained VGG19 model and a naïve Inception module to efficiently detect major kidney diseases from CT images. We customized the architecture of the VGG19 model by removing the fully connected layers and placing a randomly initialized naïve Inception module and other layers such as batch normalization, dropout and dense layers to avoid vanishing gradient and overfitting problems and to improve performance. We consider two ways of TL: feature extractor and fine-tuning, which is technically implemented and evaluated. Grad-CAM and feature map techniques are used to visualize regions of interest that are meaningful for the diagnosis of kidney disease. 4000 kidney CT images were used to evaluate the performance of the proposed model using different performance metrics. Experimental results show that the proposed model exhibits superior performance after applying fine-tuning compared to other method. The results showed that the proposed model with a fine-tuning strategy produced an excellent accuracy of 99.25% in detecting three different kidney diseases from CT images.

Modeling a Fine-Tuned Deep Convolutional Neural Network for Diagnosis of Kidney Diseases from CT Images

COVID-19 is the most recent coronavirus-related disease that has been declared a pandemic by the World Health Organization (WHO), causing a global emergency that has resulted in a large number of deaths and is rapidly spreading around the world. It causes respiratory illness and is highly contagious, putting a strain on health and medical systems worldwide. With the help of various deep learning (DL) techniques, chest CT scans are considered an effective tool for diagnosing COVID-19 because it directly affects the lungs. In addition, the visual similarities between COVID-19 and pneumonia make identification even more challenging, as COVID-19 is also a virus. In this paper, we designed a unique lightweight DL model named CVD19-Net with fewer layers as an accurate diagnostic method for COVID-19. Different regularization techniques such as dropout layers, batch normalization layers and data augmentation are injected into the CVD19-Net model to improve classification accuracy and reduce overfitting. We considered three different publicly available datasets for our experiments. (1) Dataset 1: 2482 CT images were collected; (2) Dataset 2: 7544 CT images were collected; (3) Dataset 3: 3190 CT images were collected. The experimental results show that the proposed model achieves 98.59% accuracy on dataset 1, 98.21% on dataset 2, and 95.61% on dataset 3, which is better than the existing methods. The proposed model requires less training time and storage space, which makes it computationally efficient while maintaining a high level of accuracy, which can help clinicians quickly identify COVID-19 patients.

CVD19-Net: An Automated Deep Learning Model for COVID-19 Screening using Chest CT Images

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