X-Ray Image Classification for COVID-19 Using Transfer Learning

SARS-CoV-2, the cause of one of the significant pandemics in history, first appeared in Wuhan, China. It spreads rapidly, with symptoms like fever, cough, tiredness, and loss of taste or smell. We came up with many measures where the most effective was vaccines. Yet it's not enough against the rapidly appearing waves of SARS-CoV-2. A deep learning algorithm has proven efficient in detecting Covid-19 based on pneumonia and respiratory problems. These problems have been identified with the help of CT scans and X-ray images. It'll make it a lot easier to determine who's Infected and would save a lot of time and expenses overall would provide for extensive relief in the Covid-19 pandemic. This paper uses publically available COVID-19 X-Ray and CT Scan images to create a dataset. The Deep Learning based model is used to train and test the dataset. In the experiment, the overall accuracy is 98%, and in the testing process, the overall accuracy is 99%. © 2023 The authors and IOS Press.

IoMT with Deep CNN: AI-Based Intelligent Support System for Pandemic Diseases

The Internet of Medical Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly concentrates on the integration of medical things for servicing needy people who cannot get medical services easily, especially rural area people and aged peoples living alone. The main objective of this work is to design a real time interactive system for providing medical services to the needy who do not have a sufficient medical infrastructure. With the help of this system, people will get medical services at their end with minimal medical infrastructure and less treatment cost. However, the designed system could be upgraded to address the family of SARs viruses, and for experimentation, we have taken COVID-19 as a test case. The proposed system comprises of many modules, such as the user interface, analytics, cloud, etc. The proposed user interface is designed for interactive data collection. At the initial stage, it collects preliminary medical information, such as the pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they could get the pulse oxygen level. With the help of swap test kit, they could find COVID-19 positivity. That information is uploaded as preliminary information to the designed proposed system via the designed UI. If the system identifies the COVID positivity, it requests that the person upload X-ray/CT images for ranking the severity of the disease. The system is designed for multi-model data. Hence, it can deal with X-ray, CT images, and textual data (RT-PCR results). Once X-ray/CT images are collected via the designed UI, those images are forwarded to the designed AI module for analytics. The proposed AI system is designed for multi-disease classification. It classifies the patients affected with COVID-19 or pneumonia or any other viral infection. It also measures the intensity level of lung infection for providing suitable treatment to the patients. Numerous deep convolution neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16, and VGG 19 for better classification. From the experimentation, it observed that ResNet101 and VGG 19 outperform, with an accuracy of 97% for CT images. ResNet101 outperforms with an accuracy of 98% for X-ray images. For obtaining enhanced accuracy, we used a major voting classifier. It combines all the classifiers result and presents the majority voted one. It results in reduced classifier bias. Finally, the proposed system presents an automatic test summary report textually. It can be accessed via user-friendly graphical user interface (GUI). It results in a reduced report generation time and individual bias.

COVID-19: X-ray image classification using transfer learning DCNN approach

COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19: Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. © 2022 Author(s).

Automatic Covid-19 Identification Using Machine Learning Techniques: Survey

The policies to manage the spread of diseases, tracking and predicting the growth of the diseases can effectively can be done by using Machine Learning techniques (ML). An ML can be built based on extended models that can be applied to diagnosis and find possible ways for the treatment of COVID-19 in worldwide countries. Machine learning techniques are used to provide the problems of real world by developing intelligence techniques. This survey targets on working procedure and affords the list of applications through deep convolution neural network, transfer learning, Support Vector Machine, and Linear Regression. Initially the researchers encourage the Machine learning algorithms for analyzing many domains to develop and innovates the new techniques with desirable advantages. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Automatic Prognosis of COVID-19 from CT Scan using Super-convergence CNN Algorithm

Due to the high incident rate of the novel corona virus popularly known as COVID-19, the number of suspected patients needing diagnosis presents overwhelming pressure on hospital and health management systems. This has led to global pandemic and eventual lockdown in many countries. More so, the infected patients present a higher risk of infecting the healthcare workers. This is because once a patient is positive of the virus, the recovery progress or deterioration needs to be monitored by medical experts and other health workers, which eventually exposes them to the infection. In this paper, we present an automatic prognosis of COVID-19 from a computed tomography (CT) scan using deep convolution neural networks (CNN). The models were trained using a super-convergence discriminative fine-tuning algorithm, which uses a layer-specific learning rate to fine-tune a deep CNN model;this learning rate is increased or decreased per iteration to avoid the saddle-point problem and achieve the best performance within few training epochs. The best performance results of our model were obtained as 98.57% accuracy, 98.59% precision and 98.55% recall rate. This work is therefore, presented to aid radiologist to safely and conveniently monitor the recovery of infected patients. © 2021 IEEE.

Deep Learning-based Classification of COVID-19 Lung Ultrasound for Tele-operative Robot-assisted diagnosis

Despite the implementation of strict COVID-19 guideline, over 300,000 healthcare workers has been infected with COVID-19 globally with over 7,000 deaths. This risk of infection and loss of vital healthcare workers can be eliminated by deploying a deep learning enhanced teleoperated robot. The robot for this study was developed by Worchester Polytechnic Institute, US, to be deployed for COVID-19 at the Nigerian National Hospital Abuja. In this paper, we develop a deep learning-based automatic classification of lung ultrasound images for rapid, efficient and accurate diagnosis of patients for the developed teleoperated robot. Two lightweight models (SqueezeNet and MobileNetV2) were trained on COVID-US benchmark dataset with a computational-and memory-efficient mixed-precision training. The models achieve 99.74% (± 1) accuracy, 99.39% (± 1) recall and 99.58% (± 2) precision rate. We believe that a timely deployment of this model on the teleoperated robot will remove the risk of infection of healthcare workers. © 2021 IEEE.

Diagnosis of COVID-19 and Pneumonia using Depthwise Separable Convolutional Neural Network

COVID-19 disease is a consequence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that came to light as an epidemic over the planet. The long-established diagnostic systems are confronting difficulties in identifying the virus expeditiously in the initial stages. In these circumstances, chest X-ray scans can be beneficial for the identification of COVID-19 as well as pneumonia. On that account, in this research, a deep convolution neural network having depthwise separable convolutions has been put forward to look over the chest X-ray scans for identifying COVID-19 and pneumonia precisely. The propounded model with only 0.18 million parameters has been employed on various standard datasets and performs significantly faster than other state-of-the-art models and the exploratory results explain the potency of the propounded approach. © 2021 IEEE.

Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images.

The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.