Classification of COVID19 using Chest X-ray Images in Keras
In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for educational purposes. By the end of this project, you will be able to build and train the convolutional neural network using Keras with TensorFlow as a backend. You will also be able to perform data visualization. Additionally, you will also be able to use the model to make predictions on new data. You should be familiar with the Python Programming language and you should have a theoretical understanding of Convolutional Neural Networks. You will need a free Gmail account to complete this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Convolutional Neural Network
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
NL 제공2021년 2월 19일
It is a useful project. However, the instructor speaks too fast with an accent and I couldn't catch up with her explanations.
MB 제공2021년 4월 10일
By far the best 'guided project' I have ever come across.
SP 제공2021년 11월 21일
I liked the course as it gives the complete picture of how to make prediction using convolutional neural network. Discussion forum is not so active is a negative point.
HM 제공2020년 11월 20일
It was a very good and practical project.
The subject was completely related to the current situation and I really liked this project.