A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021

1. Submitted by
Dr. C.V. Suresh Babu
G Sadhana
A study on “Impact of Artificial
Intelligence in COVID-19
Diagnosis”

2. Abstract
Lungs are one of the most vital organs in the body, yet are vulnerable
to infection and injury.
COVID-19, claiming thousands of lives all across the world.
AI can improve job efficiency by precisely identifying infections in X-ray
& CT images and allowing further measurement.
Integration of AI with X-ray and CT and help combating COVID-19.

3. Introduction
End-to-end point-of-care system for classifying and diagnosing
various respiratory disorders.
Early identification of COVID-19, that is backed by an artificial
intelligence (AI) module.
sensors to capture patients' or users' symptoms, such as body
temperature, cough sound, and ventilation.
captured data will subsequently be converted to health data and
analyzed by a machine learning module to identify patterns and
classify the combined symptoms for various respiratory disorders,
including COVID-19

4. Literary Review
Overview of the Literature 1
• AI system that can diagnose COVID-19 pneumonia using CT scan.
• Prediction of progression to critical illness.
•Potential to improve performance of junior radiologists to the senior
level.
•Can assist evaluation of drug treatment effects with CT
quantification.
Overview of the Literature 2
Artificial Intelligence,
Computer Sensitivity and Specificity,
X-Rays

5. Literary Review
Overview of the Literature 3 Overview of the Literature 4
Laboratory-based
chest radiography approach
Artificial intelligence,
big data, bioinformatics,
biomedical informatics,
deep learning, diagnosis,
treatment.

6. Methodology
The goal of cough classification is to create an automatic system that can classify many
aspects of coughs, such as cough severity, time-frequency, energy distribution, and whether
the cough is wet or dry.
Distinguished by differences in cough sound.
Signal strength of wet coughs is found to be between 0 and 750 Hz.
Dry coughs is found to be between 1,500 and 2,250 Hz.
most cough recording tests have used a sampling frequency of 48,000 to 22,050Hz.

7. Implementation
Phase 1 Phase 2 Phase 3
Cough Audio Analysis Cough Segmentation & Detection
Feature Selection
Discriminant Analysis

8. Implementation
Phase 4 Phase 5 Phase 6
Mel-Frequency Spectral
Coefficients
Cough Classification
Machine Learning Data Analysis

9. Results
The collected data will be translated to health data and evaluated by a machine learning
module to find patterns and classify the combined symptoms of several respiratory illnesses,
including COVID-19.

10. Conclusion
The previous-proposed method can be very helpful in the
early detection of not only COVID 19 but also other lungs
and respiratory system-related diseases.

11. Thank you