COVID 19 Detection System

Without any doubt, the global pandemic in the contemporary period affects all walks of our life. It affected almost all the age group and all over the world with a disease and that is named coronavirus disease 2019 (COVID 19). It includes the massive viruses that are creating sicknesses to the people like colds, and issues in the respiratory system specifically, the Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS). In addition, declared a global pandemic by the world health organization (WHO). Notably, CNN is used in the COVID 19 detection system and CNN is comprehensively deployed particularly in the computer vision field.

Detection System

This process is mostly used in the exploration of visual imagery and the image classification process. The research studies in the medical imagery field include the applied CNNs to collect the finest performance. The accumulation of synthetic CXR images which are created with the functions of CovidGAN using the structural design of CNN. In addition, it includes some classifications and they are enlisted in the following.

  • CovidGAN is used in the training dataset augmentation along with the CNN model to enhance the COVID 19 detection
  • The intended auxiliary classifier generative adversarial network (ACGAN) is related to GAN and it is denoted as CovidGAN meant for the generation of synthetic CXR images
  • A CNN-based model for COVID 19 detection is created

COVID 19 Detection Procedures

  • VGG16 model
  • Modified Gaussian decay model (MGDM)
  • Pricewise linear decay model (PLDM)

VGG16 Model

The model namely VGG16 is utilized in various issues that are created in the process of deep-learning image classification. Thus, the structural designs in the smaller network are required frequently and the architectures such as

  • GoogLeNet
  • SqueezeNet

On the other hand, the finest building blocks are used in the learning process and it is considered the easy process to implement. As an additional note, VGG 16 model is deployed to acquire accuracy in COVID 19 CXR image classification.

Modified Gaussian Decay Model (MGDM)

The sum of exponential random and independent normal variables is denoted as the modified Gaussian distribution. “Z = X + Y” is the expansion of the exGaussian random variable (Z). Here, the X and Y are denoted as independents. μ is the mean and σ2 is the variance in the X Gaussian along with that Y is considered as the exponential of rate λ.

The exponential components are included in the features of this positive skew. Along with that, it includes the observation of weighted functions in the shifted exponential and that is the process of normal distribution in weight. It consists of three significant parameters are highlighted in the following.

  • Exponential decay parameter
    • μ
  • Mean of normal distribution
    • τ = 1 / λ
  • The standard deviation of the normal distribution
    • σ

K = τ / σ is denoted as the shape which is deployed in the description process of distribution. Generally, the shapes of distributions may vary from normal to exponential and it is happening through the differences in the values of parameters

Piecewise Linear Decay Model (PLDM)

Multiple linear segments are used in this modeling process of decay. So, the Gaussian and exponential curves are used in small quantities and the usage of linear segments leads to the fastest decline. The PLDM model is integrated with all these dynamics and the cumulative data based on the deceleration phase is composed and categorized into mutual segments. At the end of the process, it acquires the optimal piecewise linear which is apt for the sense of linear squares.

Techniques for COVID 19 Detection System

  • Support vector machine (SVM)
  • Square wave voltammetry (SWV) technique

Support Vector Machine (SVM)

The support vector machine is used as the classification to categorize the symptoms into the classes that are pointed out. Through this classification, it is easy to analyze the critical condition of the patient as per the symptoms such as

  • Acute respiratory syndrome
  • High fever
  • High breathing rate

All these collected datasets are transformed into the SVM classifier. SVM is used to solve this issue because the kernel trick is included in the process to convert the low dimensional input space to the high dimensional space and it includes the conversion of the non-separable problem to the separable problem. When it is the infected column or the output column it includes the one integer value to three and they are enlisted below.

  • Severely infected
  • Non-infected
  • Mildly infected

Square Wave Voltammetry (SWV) Technique

The square wave voltammetry is considered as the format based on linear potential sweep voltammetry and it is deployed in the combination of staircase potential and square wave which is applied in the fixed electrode. There are multiple applications in many research fields based on this technique and it is specifically famed in several sensing and medical communities.

When this technique is implemented then the process has resulted in high sensitivity towards the COVID 19 detection process. The techniques such as square wave voltammetry (SWV) and cyclic voltammetry (CV) techniques are used along with the anti-COVID-19 antibodies to observe the analysis of COVID 19.

Datasets are considered a substantial part of the COVID 19 detection system. Thus, our research professionals in this field have denoted some sources to get the datasets that are used to implement the research projects in COVID 19 detection system.

COVID 19 Data Sources

As the general process, the CT chest image dataset is deployed in the research that is undertaken by the China national center for bio information. Additionally, the images in this source are classified into three types such as.

  • Normal
  • Coronavirus pneumonia
  • Common pneumonia

China national center is unconfined with the data sources as a wide process to assist the researchers in to fight against the pandemic. The datasets include 6752 CT scans along with 617775 CT slices from 4154 patients and they have distributed through 999 COVID 19 patients among them 1687 normal patients and the remaining 1468 common pneumonia patients. The research simulation includes 450 images from every single class. When the selected datasets are augmented it produces 150 additional images and here we include 50 from every single class and a total of 1500 images.

Implementing Covid 19 Detection System Project

COVID 19 Recognition Challenges

When it comes to the recognition of challenges based on COVID 19 then ILSVRC is depicted as the foremost challenge in this field. So, we have stated the significant functions of ILSVRC in the following.

ImageNet large-scale visual recognition challenge is abbreviated as ILSVRC. The main objective of this challenge is to benchmark the state of the art and to stimulate the enhancement of better computer vision techniques. The significant challenge pays more attention to several tasks in the image classification it contains both allocations of the class label to the image as per the foremost object of the photograph and the object detection encompasses the confining objects along with the photograph. This ILSVRC annotation has two methods such as,

  • Object level annotation
    • It is the bounding box which is constricted with the object instance and image class label
  • Image level annotation
    • It is denoted as the binary label to view the presence and absence of the image object class

The following is about the general challenge tasks.

  • Object detection
    • It is a depiction of image classification and the bounding box about the presence of all the object
  • Image classification
    • It is the prediction of object classes in the image
  • Single object localization
    • It is a representation of image classification and the bounding box about the presence of one object

To seal all the research challenges in the field COVID 19 detection system, we have listed some simulation tools.

Simulation Tools


The implementation processes of the COVID 19 face mask detector for images are performed using OpenCV. Firstly, the face mask is trained as per the steps which are listed in the following.

  • Input image from disk is loaded
  • Faces in the images are detected
  • Then, the face mask detector is used to categorize the face whether it is with a mask or without a mask

In addition, the OpenCV tool is essential to display the image and manipulate it. Loading and preprocessing the input image are forthcoming processes in the implementation process.

  • Loading
    • As per the loading process of the image from the disk, the copy and grab frame dimensions are created to scale and display the process
  • Preprocessing
    • It is managed through the functions of OpenCV’s blobFromImage
    • The image is resized into 300 × 300 pixels for the process of mean subtraction as per the parameters
  • Face detection is used to localize the faces in images


In COVID 19 detection system, the simulation process is used by selecting adequate algorithms based on machine learning and as per the data requirements. Here, Matlab is used to select the finest algorithm which is apt for the process through the app and it is denoted as the classification app learner used for classification and regression app learner for the regression when it is based on the machine learning process.

Deep learning is considered the phase in machine learning methods and it is related to the artificial neural network along with the depiction of learning. Computer models in deep learning are used to perform tasks based on classification from the source of numeric data, text, and images. The system of learning includes three phases such as,

  • Unsupervised
  • Supervised
  • Semi-supervised

Matlab is used to perform the processes based on ResNet- 50. It is based on the convolutional neural network and it is deep for 50 layers. A residual network is abbreviated as ResNet and it is the classic neural network that is deployed as the backbone of the tasks based on computer vision. The user can load the version that is pre-trained in the network is trained a million images and above. Finally, the pre-trained network is used to categorize the images into 1000 objects.

By using all the simulation tools and algorithms and datasets our research professionals have highlighted the research topics in this field. For your quick reference, we have enlisted titles in the following,

Latest Interesting PhD Project Topics

  • Deep learning for COVID 19 detection based on CT images
    • The convolutional neural network is adopted in this process of based COVID 19 testing system
    • The performance based on various pre-trained models in CT testing and recognized as the huge amount is examined
    • The datasets are boosted with the testing power in the models
    • A priori knowledge is recommended for this model from the training and it is apt for the CT images
    • The transfer learning approach is considered as the demonstration of existing approaches in the designated literature
    • The state of the art is accomplished through this performance based on recognition
    • Satisfactory performance is revealed through this process with the sampled training datasets
  • Multimodal deep learning for diagnosis of COVID 19 pneumonia from the chest CT scan and X-ray images
    • Transfer learning is deployed in various research and it pays more attention to the single modality based on the biomarkers that are meant for the pneumonia diagnosis
    • COVID 19 virus is used to detect the CT scan and X-ray imaging based on the chest part
    • Concatenation in two various transfer learning models that are deployed as open source datasets and it includes 2500 X-ray and 2500 CT scan images
    • While classifying the X-ray and CT scan images, it is segmented into two phases such as
      • Normal pneumonia
      • COVID 19 pneumonia
    • Here, some models are utilized in the process of image recognition, and the models such as
      • ResNet50
      • InceptionV3
      • Xception
      • MobileNet
      • DenseNet121
    • In the end, it acquires the finest classification accuracy of about 99.87% in the concatenation of VGG16 and ResNet50 networks
  • Computer vision and radiology for COVID 19 detection
    • As the primary fact, an essential amount of data is required to train and generate the performance in state of art in the deep neural networks
    • ADADELTA is stimulated through this methodology to choose the good learning rate value to duck the trial and hit
    • A phase includes 128 images to train and the loss is computed through the structural design of the neural network
    • The learning rate has some value and the loss function is minimum to select the learning rate

Other Interesting Research Topics

  • Enhanced classification of COVID 19 based on an amalgamation of texture features through CT scan and x-ray images
  • Mechanical characterization of PLA using the manufacturing of 3D printed medical equipment for COVID 19 pandemic
  • COVID 19 detection through blood tests using machine learning
  • A novel approach to predict the real-time sentimental analysis through the naive Bayes and RNN algorithm in the COVID pandemic
  • Detecting COVID 19 and community-acquired pneumonia using chest CT scan images with deep learning

So far, we conversed about the research topics that are commonly stated in the COVID 19 detection system. But the research project titles based on the COVID 19 detection system using CT images are enlisted below for your reference.

What are the Topics for the Detection of COVID 19 using CT images?

  • The basic notion in the image classification for COVID 19 patients using the chest CT scan and convolutional neural network
  • LCOV-NET: Lightweight neural network for COVID 19 pneumonia lesion segmentation from 3D CT images
  • Detection of COVID 19 from CT images through the novel LeNet-5 CNN architecture
  • Efficient classification approach based on COVID 19 CT images analysis along with the deep features
  • COVID-19-CT-CXR: A freely accessible and weakly labeled chest X-ray and CT image collection on COVID 19 from the biomedical literature

The aforementioned are the notable research topics that are dominating the research platform. In the following, we have highlighted the research areas based on COVID 19 and it is easy for the research scholars to select the topics.

What are the COVID 19 Research Areas?

  • Artificial intelligent
  • Data mining
    • Sentiment analysis of COVID 19 tweets
  • Healthcare

Overall, we support you in both the research and development aspect of your research. In addition, we are also providing assistance in paper writing, paper publication, thesis writing, and more services. For all these services, we allocate a team of experts for a specific department such as technical professionals, developers, native writers, and experts to give you the best guidance in each phase of research. Now, let us discuss the result parameters that are used to analyze the results acquired in the research.

Result Parameters

  • Regularization
  • Loss functions
  • Learning rate
  • Corona score

Regulation is considered the mechanism that is capable to adjust the mapping process and mitigate through the fitting. The training datasets are enhanced through the reduction of overfitting is called data augmentation and it is a massive memory cost for the huge concern. In addition, this regularization process is deployed for the regardless size of training datasets. There are two significant regularization techniques as

  • Drop connect
  • Dropout

The networks which are connected fully are recognized as the dropout mechanism and it is functioning as per the probability distribution of the connections in all the layers. The connections are dropped in the process and nodes are dropped in the result.

Loss Functions

Loss functions are a method that is used to calculate the performance in the algorithm based on the dataset training and its usage. It is denoted as the evaluation of prediction in the reality. Later on, it is deployed in the optimization of the algorithm by minimizing the incurred loss. The loss functions are denoted as the weather tuning indicator algorithm and it is beneficial in the process. In addition, this process includes some categories in loss functions such as.

  • Multi-class classification
  • Regression
  • Binary classification
Learning Rate

The learning rate is considered the critical value while the weights formulas are get updated and the optimal values are often in the unreachable state. The learning rate includes both low and high values and it leads to some issues. On a brief note, the low values lead to a decrease in the speed of the training process, and that results in the delay of the process. On the other hand, the high value will cause an increase in convergence speed and reduce the set weights. Finally, it leads to nope achievement in the suboptimal weight.

Corona Score

Corona score is denoted as the novel measure that is stated to analyze the severity of the lung virus. It is based on the capacity of the lungs and the capacity of the infected phase that is analyzed through the segmentation block.

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