Data Science: CNN & OpenCV: Breast Cancer Detection
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
- Basics knowledge of Python, Neural Networks and OpenCV is recommended
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 !!
[Music : bensound]
Who this course is for:
- 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.
My name is Jay and I am super-psyched that you are reading this!
Professionally, I am a Test Architect and Machine Learning Engineer. I have around 10+ years of experience in field of Test Automation and Data Science and Machine Learning.
I have professional experience of training students in the field of Data Science and Test Automation.
I am absolutely and utterly passionate about Artificial Intelligence and Machine Learning and I am looking forward to sharing my passion and knowledge with you.