Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Table of Contents (13 chapters)

Using decision trees to diagnose breast cancer

Now that we have built our first decision tree, it's time to turn our attention to a real dataset: the Breast Cancer Wisconsin dataset (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)).

This dataset is a direct result of medical imaging research, and is considered a classic today. The dataset was created from digitized images of healthy (benign) and cancerous (malignant) tissues. Unfortunately, I wasn't able to find any public-domain examples from the original study, but the images look similar to the following one:

Breast cancer tissue samples from Levenson et al. (2015), PLOS ONE, doi:10.1371/journal.pone.0141357. Released under CC-BY.

The goal of the research was to classify tissue samples into benign and malignant (a binary classification task).

In order to make the classification task feasible...