Diabetic Retinopathy Detection with Artificial Intelligence
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.
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.
Skills you will develop
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In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Understand the Problem Statement and Business Case
Import Libraries and Datasets
Perform Data Exploration and Visualization
Perform Data Augmentation and Create Data Generator
Understand the Theory and Intuition Behind Convolutional Neural Networks
Build a ResNet Deep Neural Network Model
Compile and Train the Deep Neural Network Model
Assess the Performance of the Trained Model
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