Convolutional Neural Networks for Medical Images Diagnosis
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.
Requirements
- Have the basic knowledge about CNN
- Familiar with Python programming
- Spyder editor with Python 3.7
Description
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.
Who this course is for:
- This course was designed for students who are interested in the applications of CNN to solve real-world medical diagnosis problem.
Instructor
- 4.3 Instructor Rating
- 189 Reviews
- 816 Students
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Dr. Hussein received his Bachelor's degree in Computer Engineering, Master's degree in Computer Engineering. He earned his Ph.D. in Computer Vision and Machine Learning.
Currently, Dr. Hussein is working as a research scientist at SDAIA-KFUPM Joint Research Center for Artificial Intelligence. He engaged in various research at the intersection of computer vision, deep learning, UAV navigation and swarming, and face detection and recognition.