Breast Cancer Classification with Deep Learning - Keras and Tensorflow

Want to write a research-grade Deep Learning classifier?

As Data Scientists and Machine Learning Engineers - we're exploring prospects of applying Machine Learning algorithms to various domains and extracting knowledge from data. Fast, accurate and early diagnosis of cancer improves the probability of survival, and early Breast Cancer diagnosis can save up to 400,000 lives every year. Machine Learning models can be deployed globally or locally, and can process large sums of data in a fraction of the time it takes humans.

Invasive Ductal Carcinoma (IDC) is the most common subtype of all breast cancers. Breast cancer is the most common form of cancer in women. Accurately identifying and categorizing breast cancer subtypes is an important clinical task, and automated methods can be used to save time and reduce error.

As a Machine Learning practitioner, you can help make a difference.

In this guided project, we'll be working within the field of Medical Imaging Diagnosis, tackling the classification of one of the major groups of cancer - breast cancer.

In our Guided Project, Breast Cancer Classification with Keras and TensorFlow, we'll be diving into a hands-on project, from start to finish, contemplating what the challenge is, what the reward would be for solving it. Specifically, we'll be classifying benign and malignant Invasive Ductal Carcinoma from histopathology images. If you're unfamiliar with this terminology - no need to worry, it's covered in the guided project.

We'll start out by performing Domain Research, and getting familiar with the domain we're trying to solve a problem in. We'll then proceed with Exploratory Data Analysis, and begin the standard Machine Learning Workflow. For this guide, we'll both be building a CNN from scratch, as well as use pre-defined architectures (such as the EfficientNet family, or ResNet family). Once we benchmark the most promising baseline model - we'll perform hyperparameter tuning, and evaluate the model.

Note: This Guided Project is part of our in-depth course on Practical Deep Learning for Computer Vision and assumes that you've read the previous lessons or have that prerequisite knowledge from before.

Turn Theory Into Practice

All great learning resources, books and courses teach you the holistic basics, or even intermediate concepts, and advise you to practice after that. As soon as you boot up your own project - the environment suddenly isn't as pristine as in the courses and books! Things go wrong, and it's oftentimes hard to pinpoint even why they do go wrong.

StackAbuse Guided Projects are there to bridge the gap between theory and actual work. We'll respect your knowledge and intelligence, and assume you know the theory. Time to put it into practice.

When applicable, Guided Projects come with downloadable, reusable scripts that you can refer back to whenever required in your new day-to-day work.