This project focuses on the development of a convolutional neural network (CNN) to detect pneumonia from chest X-ray images.
Leveraging TensorFlow and Keras, the model aims to assist medical professionals by providing an automated method to identify pneumonia, potentially improving diagnostic accuracy and speed.
Pneumonia is a severe lung infection that can be life-threatening if not diagnosed and treated promptly. Chest X-ray imaging is one of the most common methods used to detect pneumonia. This project utilizes a convolutional neural network (CNN) to analyze chest X-ray images and classify them as either pneumonia-positive or pneumonia-negative
The dataset used in this project is the Chest X-ray Images (Pneumonia) dataset, which is publicly available on Kaggle. The dataset contains a total of 5,863 X-ray images, which are divided into the following categories:
Training set: 5,216 images Validation set: 16 images Test set: 624 images
The images are categorized into two classes:
Pneumonia: X-rays showing pneumonia symptoms
Normal: X-rays without pneumonia symptoms
Dealing with Image data sets.
Performing Data Processing and Augmentation as and when required.
Creating and training a Convolutional Neural Network using Tensorflow 2.0
Pneumonia is an infection that inflames air sacs in one or both lungs infection can be life-threatening to anyone. The germs that cause pneumonia are contagious. Both viral and bacterial pneumonia can spread to others through: Inhalation of airborne droplets from a sneeze or cough. By coming into contact with surfaces or objects that are contaminated with pneumonia-causing bacteria or viruses
Symptoms :
Coughing that may produce mucus, Sweating or chills, Shortness of breath, Breathe or cough, Feelings of tiredness or fatigue, Loss of appetite, Nausea or vomiting & Headaches
Physical Check-Ups :
Chest X-ray, Blood Culture, Sputum Culture, Pulse Oximetry, CT scan, Fluid sample & Bronchoscopy.
An X-ray exam will allow a doctor to see lungs, heart and blood vessels etc to determine the presence of pneumonia.
Infiltrates that identify an infection.
Low contrast, overlapping organs and blurred boundary.
Medical value and application significance to construct a stable and accurate automatic detection model of pneumonia.
Environment:-
‘Google’s Colaboratory’ is the developer's best friend when it comes to deep learning.
Colaboratory is a Google research project created to help machine learning education and research.
Hosted on Google Cloud instances which we can use for free.
Google Colab and GPUs.
Datasets consist of Images, Text, and Videos.
to Set up the environment for Solving Deep Learning Problems.
How to use Tensorflow to build Convolutional Neural Networks.
Identify and detect pneumonia from the Chest X Rays using the ResNet Architecture.
Real time Response using APIs.