This project focuses on diagnosing pneumonia from chest x-rays using machine learning, particularly a CNN-based VGG19 model to enhance detection accuracy. The data utilized for the study comprises 1341 normal and 3875 pneumonia-infected chest x-ray images sourced from Kaggle, followed by extensive preprocessing. The results highlight the potential for improved recall in pneumonia detection compared to existing methods.

1. Diagnosing Pneumonia
from Chest X-Rays using
Neural Networks
 National College Of Ireland
 Module : Advance Data Mining
 Team - C
 Ashish Soni: x18136664 , Tushar S. Dalvi |
x18134301, Shantanu Deshpande |
x18125514, Yash Iyengar | x18124739

2. Contents
Introduction
Research Question
Data Gathering
Data Pre-processing
Results

3. Introduction
Radiology is a branch of medicine where the disease
diagnosed by examining X-ray Images.
To reduce the human eye error in diagnosing the disease
computer aided system has evolve for better diagnosis.
Machine learning techniques has shown remarkable
results in the recent years.
In this project we are trying to diagnose the Pneumonia
from chest X-ray using Machine Learning techniques.

4. Research
Question
 Can the recall be improved for Chest X-ray
Pneumonia Detection using CNN based VGG19
model as compared to the state of the art
technique?

5. Data
Gathering
Data for this project is extracted from
Kaggle.
It contains 1341 normal lung images of X-
rays and 3875 pneumonia infected images.
Images are in grayscale format and of
varying sizes.

6. Data
Preprocessing
Rescaling of the images is done by dividing each
image by 255.
Images are resized to 128 by 128 pixels.
Rotation of images performed by 20 degrees
horizontal rotation.
Width and height shifted by 0.2 fractions of the
total width and with the input flipped horizontally.

7. Results
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)

8. Acknowledgement
We would like to express our sincere gratitude
to Mr. Noel Cosgrave for his mentorship and
guidance.