COVID 19 Prediction and Detection Using Deep Learning

What is COVID 19 Detection? Coronavirus is based on the RNA type virus and it is the source for a comprehensive range of respiratory infections. In addition, it is considered the main cause of pneumonia disease. Artificial intelligence models are used in the biomedical field analysis process. Let’s know about the significance of COVID 19 Prediction and Detection Using Deep Learning in the following. From our research professional’s point of view, we discussed here the fundamental data in COVID 19 detection. In addition, the research scholars can contact our well-knowledgeable and updated technical team to know more about it.

Introduction to the COVID 19 Prediction and Detection

Artificial intelligence is deployed in the process of accelerating biomedical research. The interpretation of artificial intelligence and deep learning approaches are used in the process of various applications such as,

  • Image segmentation
  • Data classification
  • Image Detection

The people who are affected by COVID 19 will also suffer from the pneumonia due to the widespread of the virus in the lungs. Various deep-learning studies are used in the detection process along with the help of chest X-ray images.

When we start talking about COVID 19 prediction and detection using deep learning, the discussion never ends without the evaluation of the COVID 19 datasets. The below-mentioned datasets are more important to implement the COVID 19 prediction and detection process.

COVID 19 Datasets

  • AI for COVID imaging archive
  • COVID-CT-MD
  • SARS-CoV-2-CT-scan dataset

AIforCOVID Imaging Archive

AIforCOVID imaging archive is deployed for the massive collection of medical images of Italian COVID 19 patients, which is parallel to the clinical data. The data is liberally accessible to search and download and it is deployed in the functions such as education, scientific and commercial. DICOM is the format for the sample data based on chest X rays.

COVID-CT-MD

The COVID 19 CT scan dataset is mentioned as COVID-CT-MD and includes data about healthy patients, COVID 19 affected patients, and community-acquired pneumonia (CAP) affected patients. This COVID-CT-MD dataset is proceeding along with the process such as patient level, slice level, and lobe level labels and that includes the potential to assist the research based on COVID-19. In addition, it can support the enhancements such as deep neural networks (DNN) and machine learning processes. The datasets based on COVID-CT-MD include 169 confirmed COVID-19-positive cases, 60 community-acquired pneumonia cases, and 60 normal cases.

SARS-CoV-2CT-San Dataset

The SARS-CoV-2-CT-scan dataset includes 2482 CT scans collected from the 1252 CT scans collected from 120 patients along with that 1252 CT scans from 60 patients who are affected by SARS-CoV-2 from both males and females. Additionally, 1230 CT scan images from 60 patients who are not affected by SARS-CoV-2 males and females. In this dataset, the images include digital scans to print the CT exams and they are based on the nonstandard image size.

We provide the finest research projects that effectively convey the research problems that we found in the previous paper, the techniques we used, and the notable results that we acquired for the research project. These are the best qualities of the research work which are expected by journal editors and reviewers. So, you can contact us for your inquiries regarding your paper publication process too. Our technical experts provide the required guidance for your whole research publication work. We have well-experienced technicians in this field to complete the research project and they have listed the methods that are used in the COVID 19 detection process.

Methods for COVID 19 Detection

  • Conditional generative adversarial network
    • It is denoted as the novel method that is used to detect pneumonia and COVID 19 through chest X-ray images. In addition, the three-step process and the steps such as
      • Segmentation
        • The raw X-ray images are segmented through the functions of the conditional generative adversarial network (C-GAN) to acquire the lung images
      • Combination
        • The segmented lung images are combined with the significant deep neural networks and some extraction methods to extract the discriminatory features
      • Classification
        • The classification stage is used to categorize the normal lung images, COVID 19 and pneumonia
  • COVID 19 in X-rays using nCOVnet
    • The neural networks based on deep learning are related to nCOVnet and it is the fast screening method deployed for COVID 19 detection through analyzing the patient’s X-ray these are the visual indicators of chest radiography imaging of COVID 19 patients
    • nCOVnet is used by the researchers to utilize the X-rays and CT scans to analyze the lung images to detect COVID 19. The radiology specialist requires some time for the manual inspection report for the challenging task
  • Computer-aided diagnosis (CAD) scheme
    • Computer-aided diagnosis is functioning along with the chest X-ray images to detect the COVID 19 caused pneumonia. CAD is processing through the two image preprocessing methods to remove the majority of diaphragm regions such as
      • Bilateral low-pass filter
      • Histogram equalization algorithm

Next, let’s have a quick discussion about the models that are based on deep learning used in the LUS COVID 19 classification. In this research process, our research professionals have covered some required COVID 19 detection models to initiate your research mind in the right direction process.

Deep Learning-Based Models for LUS COVID 19 Classification

The deep learning-based models are used to classify COVID 19 in the LUS frame and the lung ultrasounds are considered the portable part and it is easy to purify the low-cost and non-invasive tool. The main objective of this process is to place the requirements and it is used to enhance the tools based on the computer-assisted analysis of the LUS imaging process to screen the COVID 19 patients.

COVID 19 Detection Models

  • DRE-Net
    • Details relation extraction neural network abbreviated as DRE-Net is used to extract the top-K details in CT images and acquire the image level predictions
    • The predictions are aggregated for the patient-level diagnosis
    • These models are created through the pre-trained ResNet-50 and the feature pyramid network (FPN) is extracted with the top-K data in the feature pyramid network (FPN)
  • 2D sequential CN
    • In the literature of deep learning, the CNN models are considered as one of the significant classes and they are the unique class that is feed forwarded to the neural network and is functional in the analysis process of multidimensional data
    • CNN’sNs conserve memory relative to multilayer perceptrons to share the parameters through sparse connection
    • Input images are changed with the matrix for the process using several CNN elements
  • MobileNetV2
    • It is based on the structural design of the convolutional neural network and it is in search of the finest accomplishment of mobile devices
    • The structure of MobileNetV2 includes the convolution layer along with 32 filters and it is followed through the 19 residual bottleneck layers
    • The cutting-edge performance of versatile models is developed through the functions of MobileNetV2 in various assignments and seat stamps in the range of model sizes
    • Factorization of normal form in the depth-based convolution
    • Pointwise convolution is implied in the depth of 1 x 1

Whatever the process may be, there is a list of tool that makes COVID 19 prediction and detection using deep learning an effective process to simulate its functions. In the following, choosing the exact tool is very important and apt for the selected research field. For that, we are right here to guide you with our team of experts to choose the right one according to your research topic. Let’s check out the tools which are effectively involved in COVID 19 detection process.

Tools / Toolboxes for the COVID Detection

  • Python
    • It is one of the ideal programming languages used in the process of data analysis
    • Challenges based on deep learning is using this python programming due to the access to its library
    • The personal GPU is used in the dataset preprocessing, and Jupyter notebook and anaconda navigator are used to regulate the online model training and large datasets
    • The testing and training process is used in the structural design and the process of implementation in python is functioning through the Keras package with the TensorFlow backend
  • Matlab
    • Image classification is processed through the Matlab deep learning process in the COVID 19 detection
    • Convolutional neural network includes 8 deep layers within it and it is denoted as AlexNet
    • The pre-trained version of the network which is trained is loaded into the ImageNet database

The above-specified tools are said to be popular entities in COVID 19 detection. Now, we are going to see about research topics in COVID 19 prediction and detection. Meanwhile, the research topic selection is considered a multifaceted task for the research scholars. So, our research experts have listed this for quick understanding.

What are the Topics for COVID 19 Prediction and Detection?

  • Deep recurrent neural networks through the consideration of mechanisms for respiratory anomaly classification
  • Reducing false prediction on COVID 19 detection using deep learning
  • Deep learning with hyperparameter tuning for COVID 19 cough detection
  • Deep learning for the detection of COVID 19 using transfer learning and model integration

Additionally, we have shared our observations on current evolving trends in COVID-19 and about the key essentials. Exclusively, our technical professionals have experience in both problem identification and solving research problems in all aspects. In the long run, they have gained unbelievable competency in both fundamentals and up-to-date research trends in this field and some of the research topics based on COVID 19 prediction and detection using deep learning are highlighted below,

Example Topics for COVID 19 Detection

  • COVID 19 recognition based on deep transfer learning
  • Self-supervised supersample decomposition for transfer learning with application to COVID 19 detection
  • Analysis of COVID 19 and pneumonia detection in chest X-ray images using deep learning
  • A deep learning-based assistive system to classify COVID 19 face masks for human safety with YOLOv3
  • COVID 19 prediction through chest X-ray image datasets using deep learning
  • Deep ensemble approaches for classification of COVID 19 in chest X-ray images
  • Light deep learning model architecture for chest X-ray based COVID 19 detection
  • Deep learning-based chest X-ray image as a diagnostic tool for COVID 19
  • A deep learning-based architecture for COVID 19 detection from chest CT scan images
  • Rapid COVID 19 diagnosis using deep learning of the computerized tomography scans

Below, we have itemized some research topics that we are currently working on in the COVID 19 prediction and detection using deep learning. If you want to know about more inspiring ideas then you can contact us. We are glad to assist you during your hard research times through our flawless guidance.

Research Topics on COVID 19 Detection

  • Detection of COVID 19 from Chest X-ray images using deep convolutional neural networks
  • Improved 3D U-Net for COVID 19 chest CT image segmentation
  • Automatic COVID 19 detection from X-ray images using ensemble learning with convolutional neural network
  • Contrastive cross-site learning with redesigned net for COVID 19 CT classification

Until now, we have discussed the research impacts of COVID 19 prediction and detection using deep learning. We hope that you have got the general idea to begin your research in COVID 19 prediction and detection. We are offering complete research support in research proposal writing, code implementation, paper writing, paper publication, thesis writing, and survey paper writing. So, the research scholars can confidently reach out to us without any hesitation for all your research needs.

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