Covid-19 detection by chest X-Ray

Project Title

Covid-19 detection by chest X-Ray

Project Area of Specialization Artificial IntelligenceProject Summary

The virus called the severe acute respiratory syndrome coronavirus had been discovered in late 2019. The virus which originated in China became a cause of a disease known as Corona Virus Disease 2019 or COVID-19. The World Health Organization (WHO) declared the disease as a pandemic in March 2020. The most serious illness caused by COVID-19 is related to the lungs such as pneumonia. The symptoms of the disease can vary and include dyspnea, high fever, runny nose, and cough. These cases can most commonly be diagnosed using chest X-ray imaging analysis for the abnormalities.

X-radiation or X-ray is an electromagnetic form of penetrating radiation. These radiations are passed through the desired human body parts to create images of internal details of the body part. The X-ray image is a representation of the internal body parts in black and white shades. X-ray is one of the oldest and commonly used medical diagnosis tests. Chest X-ray is used to diagnose the chest-related diseases like pneumonia and other lung diseases, as it provides the image of the thoracic cavity, consisting of the chest and spine bones along with the soft organs including the lungs, blood vessels, and airways. The X-ray imaging technique provides numerous advantages as an alternative diagnosis procedure for COVID-19 over other testing procedures. These benefits include its low cost, the vast availability of X-ray facilities, noninvasiveness, less time consumption, and device affordability. Thus, X-ray imaging may be considered a better candidate for the mass, easy, and quick diagnosis procedure for a pandemic like COVID-19 considering the current global healthcare crisis.

Considering the present pandemic situation, there is an appurtenant relationship between the detection of COVID-19 cases and chest X-ray image analysis and classification. In this work, an automatic diagnostic system has been developed using CNN which uses chest X-ray analysis results to diagnose whether a person is COVID-19-affected or normal. Preliminary analysis of this study has shown promising results in terms of its accuracy and other performance parameters to diagnose the disease in a cost-effective and time-efficient manner. Various machine learning models have also been used for the comparative performance analysis in comparison with the proposed CNN model to show its significance over these models.

Project Objectives

Deep learning has shown a dramatic increase in the medical applications in general and specifically in medical image-based diagnosis. Deep learning models performed prominently in computer vision problems related to medical image analysis. The ANNs outperformed other conventional models and methods of image analysis. Due to the very promising results provided by CNNs in medical image analysis and classification, they are considered as de facto standard in this domain. CNN has been used for a variety of classification tasks related to medical diagnosis such as lung disease, detection of malarial parasite in images of thin blood smear, breast cancer detection, wireless endoscopy images, interstitial lung disease, CAD-based diagnosis in chest radiography, diagnosis of skin cancer by classification, and automatic diagnosis of various chest diseases using chest X-ray image classification. Since the emergence of COVID-19 in December 2019, numerous researchers are engaged with the experimentation and research activities related to diagnosis, treatment, and management of COVID-19.

Project Implementation Method

Methods and Materials

Python was the ideal programming language for data analysis. The dataset was obtained from the open source Kaggle and GitHub and then merged to prepare a suitable dataset. The dataset contained CXR images of normal patients and patients with COVID-19. A CNN was used for feature extraction. The model has four Conv2D layers, one flattened layer, two dense layers, and a rectified linear unit activation function

In this study, transfer learning is also used so that the accuracy of the designed model can be compared with that of the pretrained model. For pretrained models, were used with some modifications in the final layers, and a head model from the base model. The customized final layers are average pooling, flattening, dense, and dropout. The CNN model is suitable for image feature extraction as it extracts the features of given images and learns and differentiates the images from these features.

Benefits of the Project

Researchers in have reported the significance of the applicability of AI methods in image analysis for the detection and management of COVID-19 cases. COVID-19 detection can be done accurately using deep learning models’ analysis of pulmonary CT [18]. Researchers in have designed an open-source COVID-19 diagnosis system based on a deep CNN. In this study, tailored deep CNN design has been reported for the detection of COVID-19 patients using X-ray images. Another significant study has reported on the X-ray dataset comprising X-ray images belonging to common pneumonia patients, COVID-19 patients, and people with no disease. The study uses the state-of-the-art CNN architectures for the automatic detection of patients with COVID-19. Transfer learning has achieved a promising accuracy of 97.82% in COVID-19 detection in this study. Another recent and relevant study has been conducted on validation and adaptability of Decompose-, Transfer-, and Compose-type deep CNN for COVID-19 detection using chest X-ray image classification. The authors have reported the results of the study with an accuracy of 95.12%, sensitivity of 97.91%, and specificity of 91.87%.

Technical Details of Final Deliverable

Many classification algorithms were chosen for this experiment. These algorithms are briefly described below:

• Support Vector Machines

The Support Vector Machine is efficient for both classification and regression. It is known to give good results for classification. The classes are separated by a hyper plane found by the algorithm. The Linear SVC from scikit-learn was implemented for this analysis. Data Computing and Artificial Intelligence 105 CERC 2020 Detecting Sarcasm in News Headlines. 

• Naive Bayes 

The Naive Bayes is a powerful algorithm used for classifying data based on probabilities. This algorithm is based on the Bayes theorem in statistics. The classification of the data is done using various probabilities. It is fast and scalable but is not without disadvantages as it assumes independence among predictors. It works well for small data sets and has been known to give great results. The Naive Bayes Classifier employed in all stages of the analysis are built using the scikit-learn package available in python.

• Logistic Regression

Logistic Regression is a popular classification algorithm. It belongs to the class of Generalized Linear Models. Its loss function is the sigmoid function which minimizes the results to be a value between 0 and 1. The Logistic Regression Model was implemented for this analysis using the function available in the scikit-learn package. 

• Convolutional Neural Networks 

(CNN) CNNs are very commonly used in text classification problems due to their success and great results. This has contributed to the decision to use CNN for this aspect of the experiment. In the first experiment with CNN, one CNN model is used on the features extracted using the Count Vectorizer. Another CNN model is also trained on the features extracted. The results of these two models are plotted and compared.

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Medical Core Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Partnerships to achieve the GoalRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	78000
Co-working space	Equipment	4	5000	20000
Developer	Miscellaneous	1	10000	10000
Laptops	Equipment	4	12000	48000