Convolutional neural networks (CNN’s) are the main deep learning tool to use for image processing. I recently used a CNN for my latest student project here at Flatiron and got to have a look at how they work and how they differ from dense neural networks, in addition to how they perform better when working with images and python. In my project, I was able to classify patient x-ray images to determine whether they had pneumonia or not. There are also many other uses for image processing in the medical field and in other fields of work and study. Next, we’ll try to show the simplest, most basic breakdown of some of these steps so that you can get on your way to building a CNN for image classification with Keras.

Keras makes it very simple to build a neural network and building a CNN will seem pretty familiar compared to that. Everything is pretty similar in how the flow works. There are many more options and paths to take when building the model, along with parameters to tune, but this article is just to show a basic outline of the model. Instantiate the sequential model, add your layers, flatten, output, compile and fit/train. Done. Here are the main imports you’ll need, but know that there will be several others depending on your data prep, the problem you are trying to solve, and what your output will be for the model.

import keras
from keras import layers
from keras import models
from keras import optimizers
from keras.models import Sequential

#convolutional-network #data-science #keras #cnn