Medical Image Analysis with Deep Learning — II

Note: This is a 4 part article and you can find the other articles via these links (part 1, this, part 3, part 4). I have also put together collection of these in a small booklet available via amazon, if you would like a physical copy. Please reach out to me if you have feedback to improve and provide this information to all.

In the last article we went through some basics of image-processing using OpenCV and basics of DICOM image. In this article we will talk about basics of deep learning from the lens of Convolutional Neural Nets. In the next article we will use Kaggle’s lung cancer data-set, review the key items to look for in a lung cancer DICOM image and use Kera’s to develop a model to predict lung cancer.

Basic Convolutional Neural Nets (CNN)

In order to understand basics of CNN, we need to understand what is convolution.

What is convolution?

Wikipedia defines convolution as “a mathematical operation on two functions (f and g); it produces a third function, that is typically viewed as a modified version of one of the original functions, giving the integral of the point-wise multiplication of the two functions as a function of the amount that one of the original functions is translated.” The easy way to understand this by thinking of…