Diabetic Retinopathy Detection with Artificial Intelligence

4.4
estrellas
13 calificaciones
ofrecido por
Coursera Project Network
En este Proyecto guiado, tú:

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.

Clock2 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Deep LearningMachine LearningPython ProgrammingArtificial Intelligence(AI)Computer Vision

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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