project

1. MACHINE LEARNING MODEL FOR
PNEUMONIA DETECTION FROM
CHEST X-RAY IMAGES
Batch No : 09
P . Sravan Kumar : 20RA1A0585
V . Shiva Charan : 20RA1A0578
V . Sai Shiva : 20RA1A0569
Guide : Dr . Shankar Ganesh

2. ABSTRACT
 Pneumonia is a serious respiratory infection that can lead to severe
complications if not diagnosed and treated promptly.
 Pneumonia is one of the leading infectious diseases. It is the inflammation
caused by the virus and bacteria that microscopically adversely
affect the air sacs.
 Approximately 7% of the world's population is affected by pneumonia every
year, and 4 million of the affected patients face fatal risks.
 Optimize model parameters with a focus on minimizing false positives and
false negatives.
 It can lead to improved patient outcomes, reduced hospital stays, and optimized
resource allocation within healthcare facilities.

3. INTRODUCTION
Pneumonia, a common and potentially life-threatening respiratory infection,
continues to be a major global health concern. Rapid and accurate
diagnosis is important for effective treatment and improved patient
outcomes. In the realm of medical imaging, particularly chest X-rays, there
exists a growing opportunity to leverage advanced technologies such as
machine learning for the development of automated diagnostic tools. This
study addresses the pressing need for an efficient and reliable solution by
proposing a machine learning model tailored for the detection of
pneumonia from chest X-ray images. The advent of machine learning, and
specifically deep learning techniques, has opened new avenues for
automating complex tasks, including medical image analysis. By
harnessing the power of computational algorithms, we aim to enhance the
diagnostic capabilities of healthcare professionals, reduce the time
required for analysis, and ultimately contribute to more timely interventions.

4. MOTIVATATION
 Pneumonia poses a significant health challenge globally, with timely
detection being crucial for effective treatment and improved patient
outcomes.
 Traditional diagnostic methods may be time-consuming and reliant on
human interpretation, prompting the need for advanced technological
solutions.
 Developing an automated, accurate, and accessible tool for pneumonia
detection can have a profound impact on global public health by
facilitating early interventions.
 The evolution of machine learning, particularly deep learning, has opened
up new possibilities for image analysis and pattern recognition.

5. LITERATURE SURVEY
 COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images
(2020):
Although primarily focused on COVID-19 detection, this study is relevant as COVID-19 pneumonia often presents in chest X-
rays. COVID-Net demonstrated the adaptability of deep learning models for different pneumonia types.
• A Comprehensive Review on Chest X-ray Image Analysis (2021):
• This review covered a wide range of applications of machine learning in chest X-ray image analysis, including
pneumonia detection. It discussed the evolution of models, datasets, and challenges in the field.
