Diabetic Retinopathy Detection with Artificial Intelligence

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在此指导项目中,您将:

Understand the theory and intuition behind Deep Neural Networks, Residual Nets, and Convolutional Neural Networks (CNNs)

Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend

Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.

2 hours
中级
无需下载
分屏视频
英语(English)
仅限桌面

In this project, we will train deep neural network model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect the type of Diabetic Retinopathy from images. Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world and estimated to affect over 347 million people worldwide. Diabetic Retinopathy is disease that results from complication of type 1 & 2 diabetes and can develop if blood sugar levels are left uncontrolled for a prolonged period of time. With the power of Artificial Intelligence and Deep Learning, doctors will be able to detect blindness before it occurs.

您要培养的技能

  • Deep Learning

  • Machine Learning

  • Python Programming

  • Artificial Intelligence(AI)

  • Computer Vision

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Understand the Problem Statement and Business Case

  2. Import Libraries and Datasets

  3. Perform Data Exploration and Visualization

  4. Perform Data Augmentation and Create Data Generator

  5. Understand the Theory and Intuition Behind Convolutional Neural Networks

  6. Build a ResNet Deep Neural Network Model

  7. Compile and Train the Deep Neural Network Model 

  8. Assess the Performance of the Trained Model

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