Classification of COVID by X Rays Images

Project Title

Classification of COVID by X Rays Images

Project Area of Specialization Artificial IntelligenceProject Summary

For automated identification of COVID-19 pneumonia, we suggested a system for visual diagnosis of cases of COVID-19 on chest x-ray images collected from patients with COVID-19, regular and viral pneumonia. By choosing the most appropriate attributes, the proposed solution has achieved both high efficiency and accurate result. In view of our results, because of the high overall performance, it is generally accepted that it would allow medical doctors to make decisions in scientific practice.  We assume that COVID-19 can increase the speed and precision of diagnosing cases with this computer-aided diagnostic tool. In a pandemic, where the burden of sickness and the need for prevention steps do not balance the availability of services, this may be particularly useful.

Project Objectives

The aim and objective or basically the goal of this project is basically trying to do an intelligentdiagnoseCOVID-19 infection based on the convolutional neural networks (CNNs) and machine learning techniques. The proposed model ensures an end-to-end learning schema that can directly learn discriminative features from the input chest CT X-ray images.

Our project will mainly focus on the following objectives:

1. Develop a system that takes an image of chest X-ray and it will be passed through CNN model to predict the result.
2.  If a patient has positive result, then our system will show the mark of identification of corona positive and provide best precaution for prevention of COVID-19 virus according to WHO.
3.  If a patient has negative result, then the system will show negative mark and provide some information of keeping yourself away from COVID-19 virus.

Project Implementation Method

The model has been designed to create a score in hospitalized patients. Obviously, having been structured and built upon a population of patients with a median age of 73-years-old, it applies to a population with these characteristics. To understand if it is applicable to a different subset of patients (i.e., younger patients), it should be used prospectively in a different trial and this is our goal for future research efforts.

The COVID-19 pandemic is requiring flexibility and rapid response to change. The importance of frequent communication between supervisors and employees is at an unprecedented level. Consistency in processes like performance management are crucial to maintaining engagement and normalcy to this otherwise unsettling time.Our project generally working on following phases.

PHASE I

Collection of X-Rays Image

PHASE II

Classification of Datasets

PHASE III

Analyzing and Designing Mockups

PHASE IV

Design and Development

PHASE V

Testing and SQA

Benefits of the Project

To control the spread of COVID-19, many suspected cases need to be screened for proper isolation and treatment. Considering COVID-19 radio graphical changes in X-ray pictures, we meant to build a deep learning method that could extract COVID-19’s graphical features to give a clinical analysis in front of the pathogenic test, thus saving critical time for disease control. a machine learning classification technique is used to classify the Chest X-ray images. As accuracy is the most significant factor in this issue, by taking a more prominent number of Chest X-ray images for training the network and by increasing the number of iterations.

Technical Details of Final Deliverable

We will be using Python syntax for this project. As a framework we will use Keras, which is a high-level neural network API written in Python. But Keras cannot work by itself, it needs a backend for low-level operations. Thus, a dedicated software library called Google’s TensorFlow will be used. For a development environment we will use the PyCharm and Jupyter Notebook anaconda3 for visualization. As our project is web application, that’s why we use Django which is a high level web framework.

This application should be made by the following tools and technologies:

1. Django
2. Anaconda 3
3. Keras
4. TensorFlow
5. PyCharm

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Medical , Health Core Technology Big DataOther TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	60100
Hosting+Domain	Equipment	1	7000	7000
API's	Equipment	3	15000	45000
Thesis Book	Miscellaneous	3	700	2100
Printing	Miscellaneous	600	10	6000