Convolutional Neural Networks for Medical Images Diagnosis

Convolutional Neural Networks for Medical Images Diagnosis, available at $39.99, has an average rating of 4.05, with 29 lectures, based on 164 reviews, and has 674 subscribers.

You will learn about 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. This course is ideal for individuals who are This course was designed for students who are interested in the applications of CNN to solve real-world medical diagnosis problem. It is particularly useful for This course was designed for students who are interested in the applications of CNN to solve real-world medical diagnosis problem.

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Summary

Title: Convolutional Neural Networks for Medical Images Diagnosis

Price: $39.99

Average Rating: 4.05

Number of Lectures: 29

Number of Published Lectures: 29

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

This course was designed for students who are interested in the applications of CNN to solve real-world medical diagnosis problem.

Target Audiences

This course was designed for students who are interested in the applications of CNN to solve real-world medical diagnosis problem.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Outlines

Chapter 2: Getting and Preparing Data

Lecture 1: Getting and Preparing Data

Chapter 3: CNN Architecture

Lecture 1: Overview of CNN Architecture

Lecture 2: CNN Convolutional Layer

Lecture 3: CNN ReLU Activation

Lecture 4: CNN Max-pooling Layer

Lecture 5: CNN Flattening Layer

Lecture 6: CNN Fully Connected Layer

Lecture 7: CNN Softmax Classifier

Chapter 4: CNN Training

Lecture 1: Data Normalization

Lecture 2: Cross Entropy Loss function

Lecture 3: CNN Backpropagation

Lecture 4: Python Implementation of CNN Training

Chapter 5: CNN Evaluation

Lecture 1: Performance Measures

Lecture 2: ROC Curve

Lecture 3: Python Implementation of CNN Evaluations

Chapter 6: CNN Improvement

Lecture 1: CNN Hyper-Parameters Tuning

Lecture 2: Using Different Optimizers

Lecture 3: Data Augmentation

Lecture 4: Using Dropout Regularization

Lecture 5: Batch Normalization

Lecture 6: Leaky ReLU Activation

Lecture 7: Early Stopping

Chapter 7: CNN Transfer Learning

Lecture 1: Transfer Learning using Oxford VGG Network

Lecture 2: Transfer Learning using Microsoft ResNet Network

Lecture 3: Transfer Learning using Google Inception Network

Chapter 8: CNN Visualization

Lecture 1: CNN Layers Visualization

Chapter 9: CNN Deployment

Lecture 1: CNN Model Deployment

Instructors

Hussein Samma
Assistant Professor

Rating Distribution

1 stars: 3 votes
2 stars: 7 votes
3 stars: 17 votes
4 stars: 66 votes
5 stars: 71 votes

Frequently Asked Questions

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