CT Scan Analysis of Covid-19 Patients Using Machine Learning

Confronting the pandemic of COVID-19, is nowadays one of the most prominent challenges of the human species. The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is

Project Title

CT Scan Analysis of Covid-19 Patients Using Machine Learning

Project Area of Specialization

Artificial Intelligence

Project Summary

Confronting the pandemic of COVID-19, is nowadays one of the most prominent challenges of the human species. The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. publicly available COVID-19 positive cases to the best of the authors’ knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability  method in an attempt to not only gain deeper insights into critical factors associated with COVID  cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and  transparent manner to validate that it is making decisions based on relevant information from the CXR images. Importantly, our focus is to develop a system which can analyze the CT scans and XRAYS of COVID patients and through this analysis predict the future medical complications that can affect the COVID patient.

Convolution Neural Network is a type of feed-forward artificial neural network, which learns hierarchical features by iterating convolution and pooling layers until the output prediction layer is reached. While the convolution layers learn specific patterns in the input or intermediate feature map with locally connected shared weights, pooling layers reduce the feature map by spatially aggregating activations. There should be a system utilizing this method to analyze the CT scan of Covid-19 Patients and make predictions about future medical complications that could develop in a Covid-19 infected patient.  

Project Objectives

Objectives: -

• Analyzing the CT scans of SARS-CoV-2 positive patients.
• Designing/Developing a system that could have the capability to predict future medical complications in patients recovered from COVID-19.
• Providing medical experts, a more accurate and comprehensive analysis of the data gathered through X-Rays and CT-Scans. 

Project Implementation Method

Selection of database:

As the accuracy of any machine learning algorithm depends on the type and quality of data that is provided to it, the database for our experiment will be very carefully selected keeping in mind the goals that we want to achieve. We have collected and added all chest CT scan images in the database for training the system. These images and data have been gathered from open source on the web. 

Preprocessing:

The images will be converted to digital form and their Processing will take place. The steps to be taken for preprocessing are: - 

• Read image 

• Resize image

• Remove noise (Denoise)

• Segmentation 

• Morphology (smoothing edges)  

Sorting of the Database:

The images will be sorted into separate folders such as COVID Patients, Non-COVID Patients (suffering from other respiratory diseases) and Normal Persons. 

Development and Application of Machine Learning Algorithms:

Machine Learning and deep learning will be implemented through designing Algorithms based on the Convoluted Neural Networks (CNN) technique. 

Training and Testing:

Following is the proposed procedure for training and testing of the data for COVID-19 detection: 

• Collect all positive and normal images in the data folder 

• Image annotation/labelling

• Training the model based on machine learning algorithm 

• Testing 

• Exporting the result. 

Evaluation and Analysis of the Results: After gathering the images of the healthy and COVID affected persons and organizing them into the database, we analyze them through Convoluted Neural Network (CNN) and other machine learning & deep learning techniques. The results are then gathered and plotted on to the graphs for the experts to extract the relevant information from the analysis. 

Benefits of the Project

After analyzing the CT scans of Covid-19 Patients and comparing the results with other respiratory diseases patients we would be finally able to make  predictions about future medical complications that could develop in a Covid-19 infected patient even after their complete or partial recovery.  

Technical Details of Final Deliverable

Already mentioned above in the methodology section.

Final Deliverable of the Project

Software System

Core Industry

Health

Other Industries

IT , Medical

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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