Detecting COVID-19 with Chest X-Ray using PyTorch

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在此指導項目中,您將:

Create custom Dataset and DataLoader in PyTorch

Train a ResNet-18 model in PyTorch to perform Image Classification

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose. Before you attempt this project, you should be familiar with programming in Python. You should also have a theoretical understanding of Convolutional Neural Networks, and optimization techniques such as gradient descent. This is a hands on, practical project that focuses primarily on implementation, and not on the theory behind Convolutional Neural Networks. 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.

您要培養的技能

Deep LearningMachine LearningStatistical ClassificationMedical Imagingpytorch

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction

  2. Importing Libraries

  3. Creating Custom Dataset

  4. Image Transformations

  5. Prepare DataLoader

  6. Data Visualization

  7. Creating the Model

  8. Training the Model

  9. Final Results

指導項目工作原理

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