Pneumonia Detection using CNN and Transfer Learning with Inceptionv3

This repository contains code for Pneumonia detection from chest X-Ray images using Convolutional Neural Networks (CNNs) with the InceptionV3 architecture. The project is developed using Python, TensorFlow, and Keras.

The code in this repository provides a framework for building a Pneumonia detection system using CNN and InceptionV3 architecture. It includes data preprocessing, model definition, fine-tuning, training, validation, evaluation, and prediction. The dataset used in this project can be downloaded from Kaggle, and it includes chest X-Ray images with Pneumonia (Virus, Bacteria) and Normal labels.

The objective of this project is to detect pneumonia from chest X-ray images.

Dataset:

This project uses two different datasets.

The reason for using two different datasets is that the CNN model performs better with a smaller dataset, while the Inceptionv3 model requires a larger dataset to achieve better accuracy.

Transfer Learning:

Transfer Learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. In this project, Inceptionv3 was used as the pre-trained model and retrained on the dataset to detect pneumonia.

Results:

CNN model achieved an accuracy of 96.2% on the Chest X-Ray Images (Pneumonia) dataset.

Inceptionv3 model achieved an accuracy of 87.49% on the Chest X-Ray Images (Pneumonia) dataset.

Web-application:

Pneumonia detection web application is a tool used to diagnose pneumonia by analyzing chest X-ray images. This web application uses computer vision techniques such as Convolutional Neural Networks (CNN) and transfer learning to classify X-ray images as either showing signs of pneumonia or not. This web application takes an input image of a chest X-ray and uses the trained CNN model to classify the image. The output is then displayed on the web page, indicating whether the input image contains signs of pneumonia or not.

Conclusion:

Pneumonia is a serious medical condition that can be diagnosed using chest X-ray images. In this solo project, I developed CNN and Inceptionv3 models to detect pneumonia from chest X-ray images. I achieved high accuracy with both models, and the CNN model outperformed the Inceptionv3 model. This project demonstrates the effectiveness of transfer learning in medical image analysis.