Project Outline:
Pneumonia is an infection in one or both of the lungs caused by bacteria, viruses, or fungi. Symptoms of pneumonia can include fever, cough, chest pain, fatigue, and difficulty breathing. If left untreated, it can lead to serious complications and even death. Treatment for pneumonia typically includes antibiotics, rest, and fluids.
Using a Convolutional Neural Network (CNN), it is possible to classify chest x-rays to determine whether a patient has pneumonia. The CNN would take the chest x-ray images as input and classify them into two categories: pneumonia and non-pneumonia. The CNN would be trained on a dataset of chest x-ray images that have already been labeled as containing pneumonia or not containing pneumonia. Once the CNN has been trained, it can be used to classify new chest x-ray images and accurately predict whether or not the patient has pneumonia.
Applications
- it can provide a quicker, more accurate diagnosis for patients.
- helpful in emergency situations where time is of the essence.
- monitor patients for signs of worsening pneumonia, which can help ensure that they receive appropriate treatment in a timely manner.
Project Outline
Hardware Requirements :
- Graphics Processing Unit (GPU)
- Computer with at least 8GB of RAM
Software Requirements :
- Python 3.x
- TensorFlow
- Keras
- OpenCV
- Scikit-Learn
- Matplotlib
- Numpy
What you will learn?
- Design, train, and evaluate the performance of a deep learning-based convolution neural network mode
- Environment setup i.e., Installation of Anaconda / tensor Flow / Keras
- Gained an understanding of how to prepare and pre-process data for use in such architectures
- Identify potential challenges and optimization techniques for improving the accuracy and reliability of the model.
- Model deployment using Tkinter
