Data Science: CNN & OpenCV: Breast Cancer Detection

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Price: 799$

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, Open CV and Python. This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like Res Net50 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 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 Open CV. Task 20: Understanding pre-trained models. Task 21: About Res Net50 model. Task 22: Understanding Conv2D, Filters, Relu activation, Batch Normalization, Max Pooling2D, Dropout, Flatten, Dense Task 23: Model Building using Res Net50Task 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 Res Net50 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 Res Net50, Custom CNNTask 34: Predicting on the test data using both Res Net50 and Custom CNN Model Task 35: About Classification Report. Task 36: Classification Report in action for both Res Net50 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 loss Task 42: Serialize/Writing the model to disk Task 43: Loading the Res Net50 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 models 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 Automation Gig.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]