COVID 19 Detection Using Artificial Intelligence

The coronavirus disease 2019 is abbreviated into the term COVID 19. It is capable to spread from one individual to another individual among those in close contact within 6 feet of the distance. Most countries have suffered a lot due to this COVID 19 pandemic. In addition, detection is considered one of the significant techniques to control this pandemic. This article is to support the research in all aspects of research in COVID 19 detection using artificial intelligence and we have knowledgeable technical professionals for the research guidance. Let us start with the short about COVID 19 detection using AI.

COVID 19 Detection Based on AI

Artificial intelligence (AI) technology is deployed to analyze the low-dose information based on high-resolution chest radiography (HRCT) and it is considered as the shot for the detection process of the symptoms of COVID 19 pneumonia.

The diagnosis and SARS-CoV-2 are detected as a speed process along with an accurate rate of detection based on the infection status and it has a significant phase in this process to consider the disease, regulate it’s widespread, choose the adequate treatments, etc.

What are the Lists of CNN Models for COVID 19 Detection?

  • NasNetMobile
  • GoogleNet
  • DarkNet19
  • MobileNetV2
  • Xception
  • Alexnet

The above-mentioned are the major CNN models used in the process of COVID 19 detection using artificial intelligence. We provide the appropriate result through this research innovation. So, let us take a detailed note of the list of AI models used in the COVID 19 detection process.

AI Models for COVID 19 Detection

  • Stochastic gradient descent (SGD)
    • It is based on an optimization algorithm and deployed in the process of machine learning applications to identify the parameters in the model
    • In the process of COVID 19 detection, it is deployed to minimize the detached functions based on the widespread training sets
  • Root mean square propagation (RMSprop)
    • It is parallel to the momentum and it is used to reduce the actions of the y-axis and to enhance the gradient descent

The above-mentioned, CNN models and AI models are essential for the COVID 19 detection process. For your ease, we have highlighted the significance of the datasets that are used in COVID 19 detection using artificial intelligence.

Dataset Details

  • Image database resource initiative (IDRI)
  • Lung image database consortium (LIDC)

Image Database Resource Initiative (IDRI)

1018 cases are included in the image database resource initiative and all the images consist of the images collected from the clinical thoracic CT scan it is accompanied by the XML file to collect and store the results based on the two-phase image annotation process and this process is functioning through the four thoracic radiologists who are the experts in this field. The two phases are listed in the following.

  • Blinded read phase
    • All the radiologists reviewing the CT scans and the CT scans have to be categorized one among the following three categories
      • nodule <3 mm
      • nodule > or =3 mm
      • non-nodule > or =3 mm
  • Unblinded read phase
    • In this phase, all the radiologists have to review the CT scans independently and with the three radiologist’s anonymized marks to find the result
    • The main objective of this process is to recognize the possible lung nodules in all the CT scans

Lung Image Database Consortium (LIDC)

The collection of images based on the diagnostic and lung cancer broadcast thoracic computed tomography scans is called the lung image database consortium and it is functioning with the grazes based on annotation. It is denoted as the web-accessible international resource for lung cancer diagnosis and detection through the process of computer-assisted diagnostics such as,

  • Evaluation
  • Development
  • Training

We have discussed the significance of datasets in the COVID 19 detection process using artificial intelligence. Now let’s discuss the techniques based on AI to detect COVID 19. In the research process, research implementation is one of the most significant parts. The substantial techniques that we have used in the research implementation of COVID 19 detection using artificial intelligence are listed below.

Empricial Study on COVID 19 Detection Using Artificial Intelligenc

Fundamental AI Techniques for COVID Detection

  • Uniform manifold approximation and projection (UMAP)
    • It is one of the significant techniques based on the nonlinear dimensionality reduction technique for the images
    • The penetration of low dimensional projection based on data is considered as the feature space for UMAP and comparatively, it is bordering real-time through the functions of fuzzy topological structure
    • The trained datasets are used in UMAP through the functions of the standard preprocessing process to recognize COVID 19 patients from normal patients
  • Agent architectures and cognitive architectures
    • COVID 19 detection neural network (COVNet) is of the significant developments that are used to extract the visual features in the volumetric chest CT scans to detect COVID 19
    • CT scans based on community-acquired pneumonia and other irregularities in non-pneumonia are occurred in the robustness test in the model
    • It is capable to extract the two processes such as
      • 3D global feature representation
      • Two-dimensional local features
    • RestNet50 is included in the framework of COVNet and it proceeds with the feature generation and the input as a series of CT slices
    • Max pooling operation is used for the functions of sliced extracted features
    • The probability score for all the types are generated through the functions of softmax activation and connected layers through the final feature map
  • Intelligent agent paradigm
    • The system based on treatment and diagnosis is functioning through the approaches based on Artificial Intelligence
    • But, there are various challenges are created in the field of science through the pandemic
    • The enhancement of intelligence system is assistive for the general practitioners for the process such as
      • Prediction
      • Diagnosis
      • Monitoring
    • It is used to reduce the pressure on the healthcare system
    • HH-COVID 19 is an intelligent model that is deployed to calculate the effects of COVID lockdown deliberations
  • Markov decision process
    • The Markov decision process and multi-armed bandits are the tools based on the machine learning approach and it is deployed to enhance and accelerate the adaptive clinical trials
    • Particularly, the multi-armed bandits are used in the context-based in clinical trials
    • The Markov decision process is used to formulate the allocation process of patients
    • This process includes three significant areas of challenges such as
      • Clinical trials for the various drugs to treat COVID 19
      • Enduring clinical trials for non-COVID-19 drugs
      • Clinical trials for repurposing drugs to treat COVID 19
  • Support vector machines (SVM)
    • SVM is monitored through the machine learning algorithms and that is deployed in both the challenges such as
      • Regression
      • Classification
    • It is used to organize the observations based on individuals
    • SVM is representing the accuracy in the detection of 32 features for COVID 19 detection
  • Heuristics
    • The heuristic is an algorithm it is used to provide the appropriate solution for classical methods based on optimization and that too in the appropriate time frame
    • It is deployed to comprehend the extraction of sub-pixel edges
    • It is used to connect the pixel points that are out of the way in the image edge while extracting the image edges
    • Mainly, it is deployed to enhance the accuracy of segmentation in the image and to accumulate the algorithms based on optimization to develop the COVID 19 detection system performance

We provide a research project which includes all the above research techniques which are apt for the research concept. Thus, we have numerous happy customers all over the world. So, you can contact us for your enquires in this COVID 19 detection using artificial intelligence. We assist with research papers, review papers, research assignments, conference papers, etc. Now, it’s time for us to discuss the significant research topics in this COVID 19 detection process.

What are the Artificial Intelligence Based Topics for COVID 19 Classification?

  • Classifying COVID 19 positive X-ray using deep learning models
  • Self-supervised supersample decomposition for transfer learning with application to COVID 19 detection
  • Dual sampling attention network for diagnosis of COVID 19 from community-acquired pneumonia
  • Artificial intelligence applied to chest X-ray images for the automatic detection of COVID 19. A thoughtful evaluation approach
  • Real-time application for COVID 19 class detection based on CNN architecture
  • Implementation of stacking ensemble learning for classification of COVID 19 using image dataset CT scan and lung X-ray

In addition, we have extended the list of research topics in the following. Here, our team of research experts has highlighted the research topics that are used in the COVID 19 detection process. To be sure, our open-minded research topics will meet your expectation in all the characteristics of the research requirements. Below, we have listed a few research topics based on COVID 19 detection using artificial intelligence.

List of AI-Based Topics for COVID 19 Detection

  • Classification of COVID 19 cases using fine tune convolution neural network (FT-CNN)
  • Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college-age students using artificial intelligence technology
  • Convolutional sparse support estimator-based COVID 19 recognition from X-ray images
  • Detection of COVID 19 patients using chest X-ray images with convolution neural network and mobile net
  • COVID 19 detection in X-ray images using CNN algorithm
  • Using CNN-XGBoost deep networks for COVID 19 detection in chest X-ray images
  • COVID 19  detection using feature extraction and semi-supervised learning from chest X-ray images
  • Development and evaluation of an AI System for early detection of COVID 19 pneumonia using X-ray

Moreover, our research professionals have acknowledged a range of enthusiastic topics and other multifaceted approaches based on COVID 19 detection. If you want to be aware of more exciting research topics then communicate with our research specialists. We are pleased to give our quick response with detailed elucidation. And here, we have itemized certain research topics rated most important by our research experts in the COVID 19 detection using artificial intelligence along with the implementation process.

AI-Based COVID 19 Detection Topics

  • COVID 19 screening using residual attention network an artificial intelligence approach
    • It is initiated through the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The proposed system is presenting a novel technique to monitor COVID 19 using artificial intelligence. This technique required some seconds to screen for the occurrence of a virus in a patient. It is used to collect the dataset-based chest X-ray images that are trained through the deep convolutional neural network-based models for the categorization of chest X rays
  • Artificial intelligence for the detection of COVID 19 pneumonia on chest CT using multinational datasets
    • Chest CT is considered the topical diagnostic tool in clinical management based on COVID 19 for lung disease. AI is capable to evaluate the CT scans for the variation among clinical entities and COVID 19 findings. The series of deep learning algorithms are used to train the miscellaneous and worldwide group of 1280 patients as the localize parietal through the COVID 19 pneumonia classification process
  • COVID 19 detection from chest X-ray scans using machine learning
    • Advanced artificial intelligence techniques are functional with radiography and it is used to detect disease. Pneumococcus, SARS, streptococcus, and COVID 19 patients are detected through the process

As a point of fact, there are several implementation tools used in the implementation process of COVID 19 detection. Typically, programming will be used to analyze the devices or data through customized tools. For your quick reference, our developers have listed out a few tools that are used in project implementation.

Implementation Tools

  • Python
    • It is denoted as the library for Keras, sklearn, and tensorflow
    • Below, we have highlighted the sample dependencies for the reference
      • opencv_python==
      • keras_tuner==1.0.1
      • keras_vis==0.5.0
      • tqdm==4.46.0
      • scikit_image==0.15.0
      • efficientnet==1.1.0
      • nibabel==3.1.0
      • pandas==0.25.0
  • Matlab
    • In the COVID 19 detection process, Matlab is used as per the following steps
      • Loading dataset
      • Image visualization
      • K-fold validation

If you want to know more information about the implementation tools based on COVID 19 detection process then contact us and grab some knowledge from our technical experts. Here, our technical specialists have listed down some highly used performance metrics in the process of COVID 19 detection using artificial intelligence.

Performance Metrics

  • Accuracy (Acc) = TP+TN / TP+FP+TN+FN
  • Recall (Rec) = TP / TP+FN
  • Specificity (Spec) = TP / FP+TN
  • Sensitivity (Sens) = TP / TP+FN
  • Precision (Prec) = TP / TP+FP

Top to bottom of this article gives you in-depth knowledge about COVID 19 detection using artificial intelligence and we hope this is essential data to develop a research project. In addition, if you are looking for the best paper writing and publication guidance, then you can find our service as a one-stop solution and we provide research support for all kinds of PhD accomplishments such as research paper writing, code implementation, thesis writing, and more. So, join us for a better research career.

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