Data Science: CNN & OpenCV: Breast Cancer Detection

Data Science: CNN & OpenCV: Breast Cancer Detection, available at $54.99, has an average rating of 4.05, with 48 lectures, based on 15 reviews, and has 106 subscribers.

You will learn about Data Analysis and Understanding Data Augumentation Data Generators Model Checkpoints CNN and OpenCV Pretrained Models like ResNet50 Compiling and Fitting a customized pretrained model Model Evaluation Model Serialization Classification Metrics Model Evaluation Using trained model to detect Pneumonia using Chest XRays This course is ideal for individuals who are Anyone who is interested in Deep Learning. or Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification. or Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV to build a CNN network architecture from scratch or Someone who wants to use AI to detect the breast cancer using slide images. It is particularly useful for Anyone who is interested in Deep Learning. or Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification. or Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV to build a CNN network architecture from scratch or Someone who wants to use AI to detect the breast cancer using slide images.

Enroll now: Data Science: CNN & OpenCV: Breast Cancer Detection

Summary

Title: Data Science: CNN & OpenCV: Breast Cancer Detection

Price: $54.99

Average Rating: 4.05

Number of Lectures: 48

Number of Published Lectures: 48

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 48

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will Learn

Data Analysis and Understanding
Data Augumentation
Data Generators
Model Checkpoints
CNN and OpenCV
Pretrained Models like ResNet50
Compiling and Fitting a customized pretrained model
Model Evaluation
Model Serialization
Classification Metrics
Model Evaluation
Using trained model to detect Pneumonia using Chest XRays

Who Should Attend

Anyone who is interested in Deep Learning.
Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification.
Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV to build a CNN network architecture from scratch
Someone who wants to use AI to detect the breast cancer using slide images.

Target Audiences

Anyone who is interested in Deep Learning.
Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification.
Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV to build a CNN network architecture from scratch
Someone who wants to use AI to detect the breast cancer using slide images.

If you want to learn the process to detect whether a person is suffering breast cancer using whole mount slide images of positive and negative Invasive Ductal Carcinoma (IDC) with the help of AI and Machine Learning algorithms then this course is for you.

In this course I will cover, how to build a model to predict whether a patch of a slide image shows presence of breast cancer cells with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a deep learning model using Tensorflow, CNN, OpenCV and Python.

This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like ResNet50 and at the same time creating a CNN model architecture from scratch, Model Checkpoints, model building and evaluation. Then using the trained model to detect the presence of breast cancer.

I have split and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1 : Project Overview.

Task 2 : Introduction to Google Colab.

Task 3 : Understanding the project folder structure.

Task 4 : Understanding the dataset and the folder structure.

Task 5 : Setting up the project in Google Colab_Part 1

Task 6 : Setting up the project in Google Colab_Part 2

Task 7 : About Config and Create_Dataset File

Task 8 : Importing the Libraries.

Task 9 : Plotting the count of data against each class in each directory

Task 10 : Plotting some samples from both the classes

Task 11 : Creating a common method to get the number of files from a directory

Task 12 : Defining a method to plot training and validation accuracy and loss

Task 13 : Calculating the class weights in train directory

Task 14 : About Data Augmentation.

Task 15 : Implementing Data Augmentation techniques.

Task 16 : About Data Generators.

Task 17 : Implementing Data Generators.

Task 18 : About Convolutional Neural Network (CNN).

Task 19 : About OpenCV.

Task 20 : Understanding pre-trained models.

Task 21 : About ResNet50 model.

Task 22 : Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2D, Dropout, Flatten, Dense

Task 23 : Model Building using ResNet50

Task 24 : Building a custom CNN network architecture.

Task 25 : Role of Optimizer in Deep Learning.

Task 26 : About Adam Optimizer.

Task 27 : About binary cross entropy loss function.

Task 28 : Compiling the ResNet50 model

Task 29 : Compiling the Custom CNN Model

Task 30 : About Model Checkpoint

Task 31 : Implementing Model Checkpoint

Task 32 : About Epoch and Batch Size.

Task 33 : Model Fitting of ResNet50, Custom CNN

Task 34 : Predicting on the test data using both ResNet50 and Custom CNN Model

Task 35 : About Classification Report.

Task 36 : Classification Report in action for both ResNet50 and Custom CNN Model.

Task 37 : About Confusion Matrix.

Task 38 : Computing the confusion matrix and and using the same to derive the accuracy, sensitivity and specificity.

Task 39 : About AUC-ROC

Task 40 : Computing the AUC-ROC

Task 41 : Plot training and validation accuracy and loss

Task 42 : Serialize/Writing the model to disk

Task 43 : Loading the ResNet50 model from drive

Task 44 : Loading an image and predicting using the model whether the person has malignant cancer.

Task 45 : Loading the custom CNN model from drive

Task 46 : Loading an image and predicting using the model whether the person has malignant cancer.

Task 47 : What you can do next to increase model’s prediction capabilities.

Machine learning has a phenomenal range of applications, including in health and diagnostics. This course will explain the complete pipeline from loading data to predicting results on cloud, and it will explain how to build an Breast Cancer image classification model from scratch to predict whether a patch of a slide image shows presence of Invasive Ductal Carcinoma (IDC).

Take the course now, and have a much stronger grasp of Deep learning in just a few hours!

You will receive :

1. Certificate of completion from AutomationGig.

2. The Jupyter notebook and other project files are provided at the end of the course in the resource section.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We’ll see you inside the course!

Happy Learning !!

[Please note that this course and its related contents are for educational purpose only]

[Music : bensound]

Course Curriculum

Chapter 1: Introduction and Getting Started

Lecture 1: Project Overview

Lecture 2: Introduction to Google Colab

Lecture 3: Understanding the project folder structure

Chapter 2: Data Understanding & Importing Libraries

Lecture 1: Understanding the dataset and the folder structure

Lecture 2: Setting up the project in Google Colab_Part 1

Lecture 3: Setting up the project in Google Colab_Part 2

Lecture 4: About Config and Create_Dataset File

Lecture 5: Importing the Libraries

Lecture 6: Plotting the count of data against each class in each directory

Lecture 7: Plotting some samples from both the classes

Chapter 3: Common Methods for plotting and class weight calculation

Lecture 1: Creating a common method to get the number of files from a directory

Lecture 2: Defining a method to plot training and validation accuracy and loss

Lecture 3: Calculating the class weights in train directory

Chapter 4: Data Augmentation

Lecture 1: About Data Augmentation

Lecture 2: Implementing Data Augmentation techniques

Chapter 5: Data Generators

Lecture 1: About Data Generators

Lecture 2: Implementing Data Generators

Chapter 6: About CNN and Pre-trained Models

Lecture 1: About Convolutional Neural Network (CNN)

Lecture 2: About OpenCV

Lecture 3: Understanding pre-trained models

Lecture 4: About ResNet50 model

Lecture 5: Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2

Chapter 7: Model Building

Lecture 1: Model Building using ResNet50

Lecture 2: Building a custom CNN network architecture

Chapter 8: Compiling the Model

Lecture 1: Role of Optimizer in Deep Learning

Lecture 2: About Adam Optimizer

Lecture 3: About binary cross entropy loss function.

Lecture 4: Compiling the ResNet50 model

Lecture 5: Compiling the Custom CNN Model

Chapter 9: ModelCheckpoint

Lecture 1: About Model Checkpoint

Lecture 2: Implementing Model Checkpoint

Chapter 10: Fitting the Model

Lecture 1: About Epoch and Batch Size

Lecture 2: Model Fitting of ResNet50, Custom CNN

Chapter 11: Model Evaluation

Lecture 1: Predicting on the test data using ResNet50 and Custom CNN Model

Lecture 2: About Classification Report

Lecture 3: Classification Report in action for ResNet50 and Custom CNN Model

Lecture 4: About Confusion Matrix

Lecture 5: Computing the confusion matrix and using the same to derive the accuracy, sensit

Lecture 6: About AUC-ROC

Lecture 7: Computing the AUC-ROC

Lecture 8: Plot training and validation accuracy and loss

Lecture 9: Serialize/Writing the model to disk

Chapter 12: Using ResNet50 model to detect presence of malignant cells in images

Lecture 1: Loading the ResNet50 model from drive

Lecture 2: Loading an image and predicting using the model whether the person has malignant

Chapter 13: Using custom CNN model to detect presence of malignant cells in images

Lecture 1: Loading the custom CNN model from drive

Lecture 2: Loading an image and predicting using the model whether the person has malignant

Chapter 14: Future scope of work

Lecture 1: What you can do next to increase model’s prediction capabilities.

Chapter 15: Project Files and Code

Lecture 1: Full Project Code

Instructors

AutomationGig .
ELEARNING HUB

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