Image classification using Deep learning

Computer vision

Stuck on a project? Get help from Skilled & Vetted Computer vision expert on Codersarts

Need Help?

Show projects

Prerequisite :

You must have python 3.7 or more installed on your system.
You must have Spyder, jupyter notebook or pycharm installed on your system. Spyder or jupyter notebook come up with anaconda. you just need to launch them after installing anaconda.
If you work on a colab no need to install python or Any other IDE, you need to just sign in with google colab.
A Kaggle account is a must to import the data from kaggle.

What you’ll learn

How to import data from kaggle on colab
How to read the image data
How to build the convolutional neural network (CNN) model
How to trained and Evaluate the model
How to check the accuracy and loss of a model by visualizing it.

Problem Statement:

Pneumonia is a respiratory infection caused by bacteria and viruses. If pneumonia is diagnosed in the early stage it will help increase the survival rate of the patients. Chest X- ray imaging is the most frequently used method to diagnose pneumonia. However, the examination of chest X-ray is a challenging task and is prone to subjective variability. Convolutional neural network (CNN) deep learning algorithm is commonly used for image classification. The focus of this project is to build the Image classification model to classify the Pneumonia and normal X-ray images.

Key highlights of projects or Essence

This project is about image classification using deep learning.
This project shows you how to use data directly from Kaggle.
This project shows you how to read the image data and visualize it.
This project show you how to use image data to train the model
This project Show you how to perform the image classification using convolutional neural network
At the end shows how to check the training accuracy training validation and training loss and validation loss by plotting the graph.