Convolutional Neural Networks for Medical Images Diagnosis

CNN, Deep Learning, Medical Imaging, Transfer Learning, CNN Visualization, VGG, ResNet, Inception, Python & Keras

This course was designed and prepared to be a practical CNN-based medical diagnosis application. It focuses on understanding by examples how CNN layers are working, how to train and evaluate CNN, how to improve CNN performances, how to visualize CNN layers, and how to deploy the final trained CNN model.

What you’ll learn

  • To build from scratch a CNN-based medical diagnosis model..
  • To learn how to get and prepare medical dataset used in this work..
  • To understand by examples how CNN layers are working..
  • To learn by examples different measures which used to evaluate CNN..
  • To learn different techniques used to improve the performances of CNN..
  • To learn how to visualize CNN intermediate layers..
  • To learn how to deploy the trained CNN model using flask API server..
  • To learn how to implement all steps using python, tensorflow, and keras..

Course Content

  • Introduction –> 2 lectures • 5min.
  • Getting and Preparing Data –> 1 lecture • 3min.
  • CNN Architecture –> 7 lectures • 16min.
  • CNN Training –> 4 lectures • 19min.
  • CNN Evaluation –> 3 lectures • 15min.
  • CNN Improvement –> 7 lectures • 14min.
  • CNN Transfer Learning –> 3 lectures • 10min.
  • CNN Visualization –> 1 lecture • 3min.
  • CNN Deployment –> 1 lecture • 5min.

Convolutional Neural Networks for Medical Images Diagnosis

Requirements

  • Have the basic knowledge about CNN.
  • Familiar with Python programming.
  • Spyder editor with Python 3.7.

This course was designed and prepared to be a practical CNN-based medical diagnosis application. It focuses on understanding by examples how CNN layers are working, how to train and evaluate CNN, how to improve CNN performances, how to visualize CNN layers, and how to deploy the final trained CNN model.

All the development tools and materials required for this course are FREE. Besides that, all implemented Python codes are attached with this course.