First and foremost, we must ensure that we’re getting guidance about the detection process of COVID 19 Detection using Deep Learning. With the ongoing trepidation of the virus, different people and researchers are coming up with different innovations and tricks to get rid of this. On the other hand, not all of the innovations will work off. We’ll now look at some detection processes based on deep learning in detail.
The COVID 19 pandemic has established the interconnected nature of our world and that is the caption,
“No one is safe until everyone is safe”
COVID 19 Detection based on Deep Learning
COVID 19 is a pandemic and imaging based on radiology has a significant phase in the performance of diagnostic tests and it is beneficial for the isolation stage, early diagnosis, and treatment in the starting stage of the disease.
Chest radiography is deployed in the process of detecting some characteristics based on the lung and it is connected with COVID 19. The notable models in deep learning are sensitive in the detection process of lung involvement in the COVID 19 and it provides a high accuracy rate in the diagnosis process.
Firstly, let us discuss the significant list of the database in COVID 19 detection process. Thus, our research experts provide detailed specifications for all such databases mentioned below.
COVID 19 Database List
- COVIDx (COVIDx CRX-2)
- COVID-CT-Dataset
- MosMedData cohort
- MDH cohort
COVIDx (COVIDx CRX-2)
It is an open-access benchmark dataset and it comprising about 13975 CXR images from 13870 patient cases. It is functional with the large number of positive cases that are publically available. While simulating the research using the COVIDX dataset, is beneficial in the evaluation process of the proposed methodology. The generated code can also be used to visualize the performance of methodology through the prevalent COVIDx dataset.
COVID-CT Dataset
As a general fact, acquiring the COVID 19 CT datasets is not a simple task because it is used to obstruct the research and developments in the AI-powered diagnosis methods of COVID 19 based on the CT images. To get rid of this issue, our research professionals have created an open-source dataset based on COVID-CT.
The COVID-CT dataset includes 349 COVID 19 CT images and they are collected from 216 patients and 463 non-COVID-19 CTs from the patients. The convenience of these datasets is confirmed through the senior radiologists who are capable to treat and diagnosing the patients who are affected by COVID 19. In addition, it is the outburst of this COVID 19 pandemic. The researchers are experimenting with the research to demonstrate further in the dataset that is beneficial in developing AI-based diagnosis models for COVID 19.
MosMedData Cohort
The MosMedData cohort dataset is also considered as the publically available dataset and 1110 subjects are comprised of 1110 CT scans. This MosMedData cohort dataset is interpreted into two significant classes such as,
- Non pneumonia
- COVID 19
In addition, MosMedData cohort dataset is used in the external testing process, and while testing the datasets it is not used in the process of model training and tuning. The model is evaluated using MosMedData cohort dataset and the result is the prediction of COVID-19 classes.
MDH Cohort
The MDH cohort dataset is also the publically available dataset based on CC-CCII. Then, quality control is used to deduct the scans which are non-standard that are considered as the small number of slices. This MDH cohort dataset includes 3953 CT scans collected from 2551 subjects. This MDH cohort dataset is making notes in three significant classes such as,
- COVID 19
- Common pneumonia
- Normal
The CC-CCII datasets are divided into three subsets such as,
- Testing
- 395 scans
- 252 subjects
- Tuning
- 352 scans
- 230 subjects
- It is used as the model checkpoint and selection process in the model
- Training
- 3206 scans
- 2069 subjects
For your ease, our experts have given a list of COVID detection models which is easy to understand the research objective. Hence, to acquire in-depth subject knowledge, you can contact our research experts. Let us take a look into the models that are deployed in COVID 19 detection using deep learning.
COVID 19 Detection Models
- ShuffleNet
- DenseNet-201
- ResNet50
- Inception V3
- DarkNet53
ShuffleNet
ShuffleNet model is based on CNN and it is used to leave behind several networks in speed with the metrics in accuracy in the same computation limitations. It is used to compose 172 layers that consist of the layers such as the max pooling layer, and convolution layer. It includes three stages along with the stack of ShuffleNet units such as,
- Output layer or softmax layer
- Fully connected layer
- Global average pooling
In addition, this ShuffleNet model is used to train and classify the CRIs into four categories such as,
- Normal
- Pneumonia bacterial
- Pneumonia viral
- COVID 19
DenseNet-201
DenseNet201 is based on the deep transfer learning process and it is proposed to categorize the patients as they are infected by COVID 19 or not and in other words COVID positive or COVID negative. This DenseNet-201 model is deployed in the feature extraction process along with its learned weights based on the ImageNet dataset and with the structural designs based on a convolutional neural network. Extensive experiments are accomplished for the performance evaluation of the DTL model through the COVID 19 chest CT scan images. While undergoing the comparative analysis the DTL based COVID 19 classification model is overtaking the competitive methods.
ResNet50
ResNet50 model is considered as the pre-trained CNNs architecture and it is exploited for the discrimination of COVID 19 from the Non-COVOD 19 from the CT lung scans and X-rays. It is the structural design of the new CNN based on the alterations in ResNet50 structures and it creates the usage of ResNet50 pre-trained models. The structural design of the ResNet50 model is developed to apt the COVID 19 dataset to enhance some layers at the end of the process.
Inception V3
The inception V3 model is projected as the innovative solution for the evaluation of complexities in the previous models with the 1×1 convolution process. The structural design of the Inception V3 model is categorized into two significant parts such as.
- Classification
- The softmax layers are essential for this process and it is fully connected
- Feature extraction
- CNN is essential for this process
- Inception V3 is the finest architecture
- The input of this process has to be an image with 299 × 299 pixels
DarkNet53
DarkNet53 is considered the CNN-based model and it is deployed in the process of deep feature extraction. This model is using two deep networks with the intersection to develop the model and the networks are listed below.
- Deep residual network
- YOLOv2DarkNet19
The DarkNet53 model has a deep structure and it includes 53 layers. This model includes the three notable elements as
- LeakyReLU layers
- Batch normalization layer
- Convolution layer
The researcher can load the pre-trained version of the trained network for more than one million images from the ImageNet database. The finest feature representation is learned by the network and it is executed in a wide range of images. The network includes the input size for the image and the size is 256 × 256. The researchers can classify the new images along with the DarkNet-53 model.
Above, we have discussed the models for COVID 19 detection. Now, our research team is capable of selecting the appropriate algorithms and models for COVID 19 detection using deep learning projects. If you want to implement your own designed model, the development team assists you in the implementation process with the apt method. For your ease, we have enlisted the list of algorithms and models in COVOD 19 detection process.
Methods and Algorithms for COVID 19 Detection
- Sine cosine algorithm
- SOM-LWL method
- Harris-hawks algorithm
Sine Cosine Algorithm (SCA)
The dimensions of the feature are reduced through the emerged sine cosine algorithm which is prepared with the adaptive beta hill climbing based on the local search algorithm. In the classification of normal and COVID 19 X-ray images are functioning with the deployment of optimized feature subset along with the vector machine classification.
This sine cosine algorithm is used to tune the parameters based on ELM and the designed network is benchmarked in the COVID-Xray-5k dataset. The results are recognized through the comparative study along with some elements. The elements are highlighted in the following.
- ELM optimization through a whale optimization algorithm
- EML is optimized via a genetic algorithm
- ELM optimized over the cuckoo search
- Canonical deep CNN
SOM-LWL Method
Self-organization map and locality-weighted learning abbreviated as SOM-LWL is the model that is used to recognize COVID 19 cases through chest X-rays. In addition, the performance of coefficient correlation is enhanced with the results among the following differences.
- COVID 19 and no finding cases
- COVID 19 and pneumonia
- Pneumonia and no finding cases
- Pneumonia cases
- No findings
- COVID 19
The model is used to collect the three key phases such as, the LWL prediction model is used to train and test the phase in decision-making diagnosis, the SOM model is used in the similarity of patients’ features based on clustering of the data instance, and the imbalanced raw dataset and feature extraction. The SOM-LWL model is categorized into three significant types of X ray labels such as,
- Pneumonia
- Microbial infection
- COVID
- COVID 19 viral infection
- Non- COVID
- Viral infection
Harris-Hawks Algorithm (HHA)
Harris Hawks optimization algorithm is abbreviated as HHA and it is used to optimize the hyperparameters. 9 pre-trained convolutional neural networks are used to apply the transfer learning and the CNNs are
- DenseNet169
- DenseNet121
- MobileNetV2
- MobileNetV1
- VGG19
- VGG16
- ResNet101
- ResNet50
- Xception
Two significant stages are used to stack the models into one model stage the compact stacking stage (CSS) and the fast classification stage (FCS). Using the 9 models the results are acquired based on the performance metrics such as,
- Area under curve
- F1-Score
- Recall
- Precision
- Accuracy
- Loss
To precede the research projects, we need to know about the significant tools that are apt for the selected research area. So, it is necessary to know about the tools for COVID 19 detection using deep learning projects. Therefore, our experts have listed down the tools used in the detection process that they consider as important in their experience.
COVID 19 Tools and Toolboxes
- Python
- Matlab
Python
The programming language python 3.6 is used to implement all the experiments along with the usage of OpenCV as the significant graphic processor software and Jupyter notebook. The available images are preprocessed to perform the hold-out validation to obtain the partitions and the classifications. DLH_COVID model network is the python code along with this the model hyperparameters are essential to reproduce the image class predictions that are associated with COVID 19 chest X-ray image classification.
The main objective of this research is to create an image classification model to detect COVID 19 and pneumonia cases using chest X-ray images. The Jupyter notebooks in python are used to cover the steps that are used in implementation, CNN architecture, and the various pre-trained models. In the end, it is used to select and create the finest image classification model and it detects COVID 19 using X-ray images.
Matlab
While implementing the COVID detection process, six types of features are extracted through the utilization of a statistical method from MATLAB named Gray level co-occurrence matrix (GLCM). All the features are used as the inputs for the initial fizzy inference model. The model is deployed to run the adaptive neuro-fuzzy inference system model for the process classification through the Matlab fuzzy logic toolbox and it is used as the implementation tool in MathWorks.
The fitVirusCV19 is used to execute the susceptible infected removed epidemic model to estimate the evaluation of epidemy. The model is anticipated as a reasonable description based on the one-stage epidemic by the tool. The fminsearch is one of the functions of the optimization toolbox and it is deployed to calculate the optimal values based on the unknown model parameters the data is plotted when the calculation is failed.
Now, it’s time to converse about the sample configuration for COVID 19 detection using a deep learning project.
Sample Configuration
- The original dataset is structured through python 3.6
- While compiling python, Jupyter notebook is used and it is one of the interface programs
- Matlab – 2019b is the software model used for the classification process for the deep learning models
- The software components are accumulated through the hardware features such as
- Intel © i5 – core 2.5 GHz
- Windows 10 operating system (64-bit)
- 1 GB graphics card
For the aforementioned tools, we have provided the configuration process for the research project. Consequently, it is vital to examine and get to know about the dependencies based on python for COVID 19 detection. Thus, the research team has listed out the python dependencies for the detection process of COVID 19.
What are the Sample Python Dependencies for COVID 19 Detection?
- vis – 0.0.5
- TensorFlow – 2.0.0
- skimage – 0.0
- scikit_learn – 0.23.2
- Pillow – 7.2.0
- deepstack – 0.0.9
- tensorflow-gpu – 2.0.0
- Keras – 2.3.1
- Numpy -1.17.0
- Matplotlib – 3.2.1
Well, we have a well-experienced research and development team in this field. Consequently, they have a strong technical groundwork in mathematical logic, numerical analysis, research source code, algorithms, and research analysis. Here, we have itemized the significant research topics and that helps to shape your knowledge based on COVID 19 detection process.
List of Topics for COVID 19 Detection using Deep Learning
- Blockchain federated learning and deep learning models for COVID 19 detection using CT imaging
- Improved classification of coronavirus disease (COVID 19) based on a combination of texture features using CT scan and X ray images
- A new classification model based on stack net and deep learning for fast detection of COVID 19 through X rays images
- COVID detection from chest x rays with deep learning CheXNet
- Deep learning techniques for the real time detection of COVID 19 and pneumonia using chest radiographs
- Control the COVID 19 pandemic: Face mask detection using transfer learning
- Detection of COVID 19 patients with convolutional neural network-based features on multi-class X-ray chest images
- Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms
- CoviNet: automated COVID 19 detection from X-rays using deep learning techniques
So far, we have discussed the research topics based on COVID 19 detection process. In addition, the following is about the research topics that are particularly based on deep learning. Consequently, you can completely consider us and give us a chance to help you to build a great research career. We are very much delighted to help the research scholars in their research. Now, we go through the research topics in COVID 19 detection using deep learning.
What are the Topics for Deep Learning Based COVID 19 Detection?
- Automated COVID 19 detection from X-rays using the deep learning techniques
- Deep learning-based diagnosis of COVID 19 through the chest CT scan images
- Deep learning for classification and localization of COVID 19 markers in point-of-care lung ultrasound
- A chest X-ray image retrieval system for COVID 19 detection using deep transfer learning and Denoising autoencoder
- COVID 19 classification using deep learning in chest X-ray images
Our knowledgeable research team supports your research work based on COVID 19 detection using deep learning with the help of many advanced technologies and algorithm parameters. Here, we have provided a sample research project that we guided recently for your reference.
COVID 19 Detection Topics
- Deep learning-based diagnosis recommendation for COVID 19 using chest X rays images
- Deep learning for screening COVID 19 using chest X-ray images
- Classifying COVID 19 positive X-ray using deep learning models
Deep Learning-Based Diagnosis Recommendation for COVID 19 Using Chest X Rays Images
In COVID 19 screening, COVID 19 patients are demonstrated as the significant substitute indicator through chest X-rays. In addition, accuracy is acquired through expertise based on the radiology system. The system based on diagnosis recommender is used to support the doctors to analyze the lunge images of the patients and that leads to the reduction of doctor’s diagnosis encumbrance. The convolutional neural network is one of the techniques in deep learning and it is used in the process of medical imaging classification. The examination of chest X-ray images was undergone four notable deep-learning CNN structural designs for the COVID 19 diagnosis. ImageNet database is used to pre-train the models and it is used to reduce the necessity of large training sets when they are pre-trained weights. It is used to observe the structural designs of CNN for the COVID 19 diagnosis.
Deep Learning for Screening COVID 19 Using Chest X-Ray Images
The extract key features, some tactics to detect the abnormalities, automated, fast processes, and more are used to modify the lung parenchyma and all these are provided through deep learning. In addition, it is based on the specific monograms of COVID 19 virus. It is used when the existing COVID 19 datasets are inadequate to train the deep neural networks. Subsequently, a novel notion is proposed and it is named domain extension transfer learning (DETL). The deep convolutional neural networks are pre-trained through the functions of DETL. While comparing DETL with the large chest X-ray dataset, DETL is used to tune the categorization among the classes such as,
- COVID 19
- Other diseases
- Normal
- Pneumonia
Classifying COVID 19 Positive X-ray Using Deep Learning Models
In general, COVID 19 is categorized as a pandemic through the uncertainty in disease-controlling options, pathogenicity, and transmission. Even though the government’s safety measures the disease is spreading all over the world and due to this the public health system gets distorted. So, all the countries are started to follow some alternative techniques to reduce the count of COVID 19 in society. Thus, this proposed work is providing some preliminary results based on deep learning models and it is used to categorize the COVID 19 positive cases through X-ray images. In addition, the metrics are used in the classification process and the metrics such as,
- F1 – score
- Specificity
- Sensibility
- Precision
- Accuracy
This proposed system resulted in the classification process and it includes two types of classification such as,
- Binary classification
- COVID 19 Vs pneumonia
- COVID 19 Vs healthy
- Multiclass
- COVID 19 Vs pneumonia Vs healthy
Through this article, we have given you a very broad picture based on the COVID 19 detection using deep learning where you can find complete research information regarding the detection process, steps and required algorithms, toolboxes, etc. We have been guiding research scholars for more than 20+ years. We have earned a huge reputation among research scholars and professors across the best universities in the world. This reputation is due to our quality, originality, novelty, on-time delivery, and more. Therefore, the research scholars can reach us to fulfill all your research requirements with the best innovations and novel executions with the support of our research experts.
