Data Science: Cnn & Opencv: Breast Cancer Detection

A practical hands on Deep Learning Project on building a Breast Cancer Detection model using Tensorflow, CNN and OpenCV

What you'll 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

Requirements

Basics knowledge of Python, Neural Networks and OpenCV is recommended

Description

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 1Task 6  :  Setting up the project in Google Colab_Part 2Task 7  :  About Config and Create_Dataset FileTask 8  :  Importing the Libraries.Task 9  :  Plotting the count of data against each class in each directoryTask 10  :  Plotting some samples from both the classesTask 11 :  Creating a common method to get the number of files from a directoryTask 12 :  Defining a method to plot training and validation accuracy and lossTask 13 :  Calculating the class weights in train directoryTask 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, DenseTask 23 :  Model Building using ResNet50Task 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 modelTask 29 :  Compiling the Custom CNN ModelTask 30 :  About Model CheckpointTask 31 :  Implementing Model CheckpointTask 32 :  About Epoch and Batch Size.Task 33 :  Model Fitting of ResNet50, Custom CNNTask 34 :  Predicting on the test data using both ResNet50 and Custom CNN ModelTask 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-ROCTask 40 :  Computing the AUC-ROCTask 41 :  Plot training and validation accuracy and lossTask 42 :  Serialize/Writing the model to diskTask 43 :  Loading the ResNet50 model from driveTask 44 :  Loading an image and predicting using the model whether the person has malignant cancer.Task 45 :  Loading the custom CNN model from driveTask 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 !![Music : bensound]

Overview

Section 1: Introduction and Getting Started

Lecture 1 Project Overview

Lecture 2 Introduction to Google Colab

Lecture 3 Understanding the project folder structure

Section 2: Data Understanding & Importing Libraries

Lecture 4 Understanding the dataset and the folder structure

Lecture 5 Setting up the project in Google Colab_Part 1

Lecture 6 Setting up the project in Google Colab_Part 2

Lecture 7 About Config and Create_Dataset File

Lecture 8 Importing the Libraries

Lecture 9 Plotting the count of data against each class in each directory

Lecture 10 Plotting some samples from both the classes

Section 3: Common Methods for plotting and class weight calculation

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

Lecture 12 Defining a method to plot training and validation accuracy and loss

Lecture 13 Calculating the class weights in train directory

Section 4: Data Augmentation

Lecture 14 About Data Augmentation

Lecture 15 Implementing Data Augmentation techniques

Section 5: Data Generators

Lecture 16 About Data Generators

Lecture 17 Implementing Data Generators

Section 6: About CNN and Pre-trained Models

Lecture 18 About Convolutional Neural Network (CNN)

Lecture 19 About OpenCV

Lecture 20 Understanding pre-trained models

Lecture 21 About ResNet50 model

Lecture 22 Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2

Section 7: Model Building

Lecture 23 Model Building using ResNet50

Lecture 24 Building a custom CNN network architecture

Section 8: Compiling the Model

Lecture 25 Role of Optimizer in Deep Learning

Lecture 26 About Adam Optimizer

Lecture 27 About binary cross entropy loss function.

Lecture 28 Compiling the ResNet50 model

Lecture 29 Compiling the Custom CNN Model

Section 9: ModelCheckpoint

Lecture 30 About Model Checkpoint

Lecture 31 Implementing Model Checkpoint

Section 10: Fitting the Model

Lecture 32 About Epoch and Batch Size

Lecture 33 Model Fitting of ResNet50, Custom CNN

Section 11: Model Evaluation

Lecture 34 Predicting on the test data using ResNet50 and Custom CNN Model

Lecture 35 About Classification Report

Lecture 36 Classification Report in action for ResNet50 and Custom CNN Model

Lecture 37 About Confusion Matrix

Lecture 38 Computing the confusion matrix and using the same to derive the accuracy, sensit

Lecture 39 About AUC-ROC

Lecture 40 Computing the AUC-ROC

Lecture 41 Plot training and validation accuracy and loss

Lecture 42 Serialize/Writing the model to disk

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

Lecture 43 Loading the ResNet50 model from drive

Lecture 44 Loading an image and predicting using the model whether the person has malignant

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

Lecture 45 Loading the custom CNN model from drive

Lecture 46 Loading an image and predicting using the model whether the person has malignant

Section 14: Future scope of work

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

Section 15: Project Files and Code

Lecture 48 Full Project Code

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.