Generally, COVID 19 is a newfangled virus that is impacting the whole world desperately as it is spreading first and foremost through contact with the person. The detection process of COVID 19 is the finest option for solving this issue. Thus, this article is based on COVID 19 detection using X-rays.
Definition of Covid-19
Coronavirus disease 2019 is abbreviated as COVID 19 and it is caused by the syndrome in the acute respiratory coronavirus 2 (SAR-Cov-2) and it is imposed as a global health predicament. The high transmission rates are capable to lead the processes such as,
- Predictive tools
- Accurate diagnosis
- Early detection needs the exigent requirements for the rising mortality
- Requisite critical care resources
- Multiple organ failures
- Respiratory distress
First and foremost, let’s discuss the datasets based on X-rays along with their functionalities. There are three significant datasets in this COVID 19 detection using X-rays and they are highlighted in the following.
X-Ray Dataset Description
The composition of the dataset requires 1124 CXR images. In addition, among these 1124 images, 721 images are based on the normal CXR and the balance 403 images are the COVID-CXR. Datasets are created through the collected images through three publicly accessible datasets.
- COVID-19 chest X-ray dataset initiative
- IEEE COVID chest X-ray dataset
- COVID 19 radiography database
The above-mentioned datasets are determined through the available public and research communities. The accumulated images are combined and the dataset removes all the duplicate images through the image hashing methods. In addition, this method is used to create the hash values to recognize the input images in the content of the image.
Remote Sensing Image Dataset
As a fact, the dataset includes images based on normal CXR, common pneumonia, and COVID 19 positive. CXR images are used to collect the datasets in 5 public databases such as
- Chest X-ray images
- Pneumonia virus Vs pneumonia bacteria
- COVID 19 radiography database
- Actualmed COVID chest X-ray dataset
- COVID chest X-ray dataset
The training set, includes 5300 images among these images, 1600 are bacterial pneumonia images, 1600 are viral pneumonia images, 1300 images are normal and 800 images are collected from COVID 19 patients.
In addition, the test set includes 741 images and it consists of 197 images based on viral pneumonia, 142 images of COVID 19 patients, 202 images related to bacterial pneumonia, and 200 normal images. The MCFF-Net process is used to accomplish the four classification experiments in the dataset based on the types of images such as.
- Viral pneumonia
- Bacterial pneumonia
- Normal
- COVID 19
At this instant, let us take a glaze over the list of models that are functional in the X-ray based COVID 19 detection.
Models for X-ray Image Based COVID 19 Detection
- CheXNet
- mobilenet-ssd
- COVID-Net
Our knowledgeable research team provides support for your research in COVID 19 detection using X-ray with the help of many advanced technologies and algorithms. In addition, our research service is enabled for all the research fields so reach us to aid more. Here we have highlighted the significant algorithms in the following.
Algorithms for COVID Detection
- Salp swarm algorithm (SSA)
- Harris hawks algorithm (HHA)
- Equilibrium optimizer (EO)
- Archimedes optimization algorithm (AOA)
- Whale optimization algorithm (WOA)
Salp Swarm Algorithm (SSA)
It is related to the Meta heuristic random population-based algorithm in the swarming mechanism of salps during the process of rummaging in the oceans. The deep learning techniques and chaotic salp swarm algorithm are enhanced through the determination of patients who are infected with coronavirus pneumonia using X-ray images. The efficientNet-B0 model is one of the deep learning methods and it is deployed to optimize the coefficient feature matrix and to diagnose COVID 19 and CSSA disease.
Harris Hawks Algorithm (HHA)
The main objective of this harris hawks optimization algorithm is to acquire the optimization of hyperparameters. Nine pre-trained convolutional neural networks are functional through transfer learning and the networks are listed in the following.
- DenseNet169
- DenseNet121
- MobileNetV2
- MobileNetV1
- VGG19
- VGG16
- ResNet101
- ResNet50
- Xception
The finest models are loaded into the system through the functions of the compact stacking stage and fast classification stage. This process has resulted in the reports through the performance metrics such as,
- Area under curve
- F1 score
- Recall
- Precision
- Accuracy
- Loss
Equilibrium Optimizer (EO)
Color image segmentation includes threshold image segmentation and it is denoted as the classic method. In addition, multi-level image segmentation requires the hybrid equilibrium optimizer algorithm. The mean and median of color images are calculated through the multi-level threshold method and it is used to enhance the accuracy of the system. It is executed in the chest X-ray scan images in the COVID 19 based dataset.
Archimedes Optimization Algorithm (AOA)
The foremost intention of optimization is to regulate the design variables and it is used to maximize and minimize the objective functions. This algorithm includes various random numbers along with some restrictions that are parallel to the metaheuristic population. The process of accelerations, random volumes, and densities have occurred in the object-searching method. The global optimization method is acquainting through the Archimedes optimizer through the process of exploration and exploitation.
Whale Optimization Algorithm (WOA)
The whale optimization algorithm is considered the nature-inspired metaheuristic algorithm. X-ray images are considered significant efforts to detect COVID 19 in the infected person.
In addition, we listed out a few major implementation tools used in the research implementation of COVID 19 detection using X-rays. Further, if you want more detail on this field then communicate with us.
Implementation Tools
- Python
- Matlab
Python
The significant python environments are installed in the process of analytical libraries and the scikit-image is considered the open-source image processing toolkit in python. It is deployed to combine the scalability performance, versatile image processing capabilities, and gentle learning curve are essential in the analysis of X-ray imaging data to acquire high throughput. The large dataset of COVID-19 X-rays is utilized in the implementation of python and it is essential to use the deep transfer learning process to obtain reliable and adequate infections based on COVID 19.
Matlab
The input X-ray images are renewed to grayscale from RGB through the functions of Matlab tools and they have to be resized as 224×224 pixels in the system and which is essential in the process of COVID 19 detection pre-processing. It is deployed to eliminate the machine annotations and superfluous text in the images. In addition, the testing and training process is processed through the extraction of the region of interest (ROI). The lung region is used to define the ROI of chest X-ray images. The tool Matlab is used to classify COVID 19.
We have more topics in COVID 19 detection using X-rays. For each topic, we have a knowledgeable research team and they assist you with the research in the process of COVID 19 detection. Our research experts provide plagiarism-free research papers. Now let us discuss the significant research ideas in COVID 19 detection process.
What are the Research Topics based on COVID 19 Detection using X-Ray?
- Accurate detection of COVID 19 using deep features based on X-ray images and feature selection methods
- Deep learning-based multimodal system for COVID 19 diagnosis using breathing sounds and chest X-ray images
- Automated image classification of chest X-rays of COVID 19 using deep transfer learning
- A preliminary analysis of AI-based smartphone application for diagnosis of COVID 19 using chest X-ray images
- Explainable deep graph diffusion pseudo labeling for identifying COVID 19 on chest X rays
Next, we are going to see about the research topics based on the X-ray images. For that, our resource teams in this field of COVID 19 detection have given you a list of topics especially they are functional in the contemporary research field.
X-ray Image-Based Research Topics
- Covid19XrayNet: A two-step transfer learning model for the COVID 19 detecting problem based on a limited number of chest X-ray images
- CoroNet: A deep neural network for the detection and diagnosis of COVID 19 from chest X-ray images
- Enhancing automated COVID 19 chest X-ray diagnosis through the image-to-image GAN translation
- COVID 19 detection through X-ray chest images
- COVIDGR dataset and COVID-SDNet methodology for predicting COVID 19 based on chest X-ray images
- Performance evaluation of transfer learning technique for automatic detection of patients with COVID 19 on X-ray images
- COVID 19 Diagnosis: Comparative approach among the chest X-ray and blood test data
- Chest X-ray classification of pneumonia and COVID 19 using modified capsule networks
- Content-based COVID 19 chest X-ray retrieval framework using stacked autoencoders
So far, we have discussed the research topics and ideas about COVID 19 detection using X-rays. In the following, our research experts have highlighted the significant research topics along with the implementation process. If you have any inquiries, you can contact our research team. Let us discuss the research topics.
Topics for X-Ray Image-Based COVID 19 Detection
- Automatic detection of COVID 19 infection using chest X-ray images through transfer learning
- A person who is infected by COVID 19 is capable to cultivate pneumonia and it is detected through a chest X-ray examination. The proposed work is based on the automatic detection of COVID infection using chest X-ray images. The datasets included in the process are 194 X-ray images from health patients and 194 X-ray images from patients who are diagnosed with COVID 19. In addition, convolutional neural networks (CNNs) are trained through the ImageNet and they become accustomed to the feature extractors in the X-ray images. Along with that, some machine learning methods are used in CNN and that is deployed to acquire the F1 score and accuracy at a huge level. Additionally, the machine learning methods are listed in the following
- Support vector machine
- Multilayer perceptron
- Random forest
- Bayes
- k-nearest neighbor
- A person who is infected by COVID 19 is capable to cultivate pneumonia and it is detected through a chest X-ray examination. The proposed work is based on the automatic detection of COVID infection using chest X-ray images. The datasets included in the process are 194 X-ray images from health patients and 194 X-ray images from patients who are diagnosed with COVID 19. In addition, convolutional neural networks (CNNs) are trained through the ImageNet and they become accustomed to the feature extractors in the X-ray images. Along with that, some machine learning methods are used in CNN and that is deployed to acquire the F1 score and accuracy at a huge level. Additionally, the machine learning methods are listed in the following
- A hybrid COVID-19 detection using an enhanced marine predators algorithm and ranking-based diversity reduction strategy
- Generally, chest X-ray scans are deployed to create an accurate analysis as fast processes and a large number of tests. The improved marine predator algorithm (IMPA) is used in the segmentation of X-ray images. The functions of IMPA acquire the finest results through the deployment of ranking-based diversity reduction (RDR). This proposed system is functional to acquire the accurate detection results
- Detection of COVID 19 patients from CT scan and chest X-ray data using modified mobileNetV2 and LIME
- The widespread transmission is the main reason for the COVID 19 pandemic and it is denoted as the challenging issue in history from the perspective of healthcare. COVID 19 patient screening is proposed through the usage of chest radiographs and chest computerized tomography. To analyze the images, deep learning is combined with artificial intelligence. This pairing process is used to acquire a high accuracy rate in the disease diagnosis. In this proposed system, the deep convolutional neural network models are used and they are listed in the following.
- VGG19
- ResNet101
- ResNet50
- InceptionResNetV2
- MobileNetV2
- VGG16
- The widespread transmission is the main reason for the COVID 19 pandemic and it is denoted as the challenging issue in history from the perspective of healthcare. COVID 19 patient screening is proposed through the usage of chest radiographs and chest computerized tomography. To analyze the images, deep learning is combined with artificial intelligence. This pairing process is used to acquire a high accuracy rate in the disease diagnosis. In this proposed system, the deep convolutional neural network models are used and they are listed in the following.
To this end, we ensure that we have provided appropriate guidance for COVID 19 detection using X-rays in terms of implementation details, and realistic results for the research performance along with the evaluation. Our technical professionals help you in all aspects of research such as the identification and investigation of new algorithms, approaches, architecture design, and so on. We assist the research scholars in selecting a topic until the paper publication within a short period and our clients endorse the plagiarism-free research work from our research experts. For more inquiries, you can contact us and we provide 24 x 7 service for research scholars and students.
