1. Topic: Unlocking the Potential of Deep Learning:
Pneumonia Detection Website Project
Group Members
Faizan Nadeem 19014065002
Zohaib Zahid 19014065010
Shan Ali 19014065017
Abdullah Sarfraz 19014065003
2. Project Overview:
In this presentation, we will be discussing the
development of a website that uses deep learning
to detect pneumonia from X-ray images. Pneumonia
is a leading cause of death worldwide, and early and
accurate diagnosis is crucial for patients' survival.
Our project aims to address this critical need by
providing a faster and more accurate diagnostic
tool, ultimately leading to improved patient
outcomes, increased efficiency, and cost savings for
the healthcare industry. We will be covering the
project overview, objectives and business
justification, project scope, risk management,
timeline and milestones, resources and budget, and
conclusion. Thank you for joining us today to learn
more about this important project.
3. Project Goals
• The project goals are to create a website
that will be able to accurately detect
pneumonia from a person's chest X-ray.
• The website will be built using deep learning
algorithms and will be able to provide an
accurate results in a short amount of time.
4. Project Scope
The project scope includes the research and development of a deep
learning model for pneumonia detection, website design and
development, integration of the deep learning model with the website,
testing and validation, and deployment and maintenance. The website
will be accessible to medical professionals and patients, and will have the
capability to process and analyze X-ray images in real-time. The project
team will also implement a comprehensive risk management plan to
identify and mitigate potential risks throughout the project. The final
deliverable will be a live website that can be used for pneumonia
detection.
5. Project Purpose:
Almost all the hospitals in Pakistan was using traditional techniques and ways to detect different
types of disease. There was a lot of human error involved especially in the detection of infectious diseases like pneumonia,
aids and so on. Radiologists were using their prior practice experiences to detect the disease. Millions of patients died due to
the human error involved in the detection of this type of infection. The radiologists can detect the disease but they weren’t
sure about where the accurate infectious area is. So, it surely is a huge loss for humanity as well as a huge flaw in our health
system. A lot of research work has been done that says millions of people died because of pneumonia and also because of
human error involved in detecting pneumonia which most by comparing any other major disease like cancer.
Objectives:
Increase the standard of detecting the disease with up to 90% accuracy
Reduced human error involved in the disease detection process by up to 60%
Deliverables:
The end product is a SAAS based web application integrated with a deep learning algorithm
that detects pneumonia its specific type with accuracy greater than 90%, User needs to buy
subscription according to its usage needs
6.
7.
8. Human Resource Management
We applied William Ouchi Z theory in which they find the
common features of America and Japan successful
organization.
1. Lifetime employment
2. Slow Evolution and Promotion
3. Carrier paths
4. Emphasis on humanity's concerns for the people
5. Collective decision mapping
6. Human resource deployment
7. Informal control
10. Project
Resources
The project will require a variety of resources to be
successful, including:
• Personnel: The project team will include individuals
with expertise
• Equipment and technology: The project will require
computer hardware and software, such as servers,
databases, and deep learning development tools.
• Data: A large dataset of X-ray images will be required
to train and test the deep learning model.
• Financial resources: The project will require funding
for personnel salaries, equipment and technology,
data acquisition, and other project-related expenses.
• Other resources: The project may require additional
resources such as office space, equipment, and
materials.
11. Conclusion:
The use of deep learning allows for more
accurate and reliable results, which can
ultimately lead to better outcomes for
patients. Furthermore, the website's
accessibility to both medical professionals
and patients makes the diagnosis process
more convenient and less time-consuming.
Overall, this project represents an important
step forward in the fight against
pneumonia and demonstrates the
potential of deep learning to improve
healthcare outcomes. We are proud to
have been a part of this project and look
forward to seeing the positive impact it will
have on patients and the healthcare
industry as a whole.
