PPT ABOUT FYP PROJECT BASED ON THE IDS SYSTEMS FYP INITIAL PRESENTATION.pptx

PPT ABOUT FYP PROJECT BASED ON THE IDS SYSTEMS

1. Title: Machine Learning For Respiratory Sound Classification Supervisor: Dr Zafi Sherhan Shah. Co-Supervisor: Dr sherham shah. Group Members Ahmad -19tl31 Aina Rao-19tl45 Shifa Mobin - 19tl23

2. Presentation Outline Background and Motivation Aims and Objective Problem Statement / Solution Methodology Relevant Work / Literature Review Visuals Future Work Conclusion Timeline References

3. Background The entire world has been impacted by the global pandemic, and unfortunately, some people have lost their lives. Even now, in China, there is a resurgence of COVID- 19 cases, which is causing breathing problems and is difficult detecting the type of infection. Abnormal breathing sounds can be a sign of serious respiratory illnesses like Pneumonia, Bronchitis, COVID-19, or Asthma, which can be fatal if not detected and treated promptly.

4. Aims and objectives: Aim of this project is to create a machine learning model that can accurately identify and classify different type of respiratory sound . By training the model on large dataset of respiratory sound we hope to enable it to distinguish between various types of sound those produce by healthy and affected lungs . Objectives: 1. To evaluate the effectiveness of machine learning model based on various factors such as size , computational complexity and classification performance . 2. To compare the performance of different machine learning model and determine which ones are most accurate and efficient at classifying respiratory sound . 3. To Develop user friendly interface that allow patient to easily record and upload their breathing sound for analysis 4. To enable heath care provider to remotely examine respiratory sound , thereby improving access to timely medical care especially in urgent cases

5. Motivation: We decided to work on this project Because: One of the main reasons for our motivation is the lack of access to physicians in urgent cases. Also, for various reasons, some people are unable to visit a physician in a timely manner. To address this issue, people can use a digital stethoscope that allows them to record their respiratory sounds and upload them to our model. By analyzing these recordings, our model can determine whether or not a physician's visit is necessary. This can help people receive timely medical attention, even if they are unable to visit a physician in person.

6. Problem Statement Every professional is bound according to its surrounding , man who is busy cannot go to the physician on time due to its busy schedule , and avoid its health life . Problem Solution We are going to propose a model which can record breathing sound and predict the disease also the severity of the health state .

7. Relevant Work/Literature Review:

8. Methodology

9. Future Work We will offer model files that can be loaded and used for prediction purpose. There are numerous approaches in the field of deep learning, including popular architechtures such as ResNet , VGG . We will thoroughly analyze the performance of each of these algorithms in terms of both accuracy and latency, in order to determine the most suitable option for our specific needs and goals.

10. Methodology for module:

11. FYP TIMELINE Task Name Jan Feb March April May June July Aug Sept Literature Review Analysing DataSets Taking embedings Surveying/Permission Hospitals & Clinics Formulating Plans/Budgets For data Collection Data Collection For Sites Data Annotation Model Training/ Optimization Thesis Writing

12. References [1] ICBHI 2017 Challenge | ICBHI Challenge . (2017). Auth.gr. https://bhichallenge.med.auth.gr/ICBHI_2017_Challenge