Opening our Eyes with Convolutional Neural Networks

Image of a lung affected by pneumonia - Source: https://www.med-ed.virginia.edu/courses/rad/cxr/pathology3chest.html

Convolutional Neural Networks

Using AI and machine learning we are now able to teach machines to do specific objectives and make predictions. The most well-known approach is through neural networks which are meant to simulate the neurons in our brain. Convolutional neural networks (CNN) are used specifically for image classification. Let’s break down how they work.

Source - https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53

How do you Recognize these Features?

Pixels in images are made up of an RGB value, which stands for red, green and blue which when combined can form any colour. When dealing with images in CNN’s we will convert images to greyscale, which is one number between 0 and 255. We will then apply a kernel filter which will locate the edges of an image.

Zooming in on greyscale pixels and applying a kernel filter

CNN’s in Action

In my project, I decided to use CNN’s to classify images from the CIFAR-10 data set which includes 10 different objects like horses, ships and other animals and vehicles. I decided to use a network with many different types and subjects in images since many CNN’s today are binary. Meaning CNN’s are only looking for one thing. If your only looking for a patient affected by pneumonia, you may not be able to tell if they have lung cancer or flail chest. That sucks! Classifying 10 different subjects is a start but what if one happens to have that 11th disease. Anyways, here is the network.

The Convolutional Part
Source- https://codegolf.stackexchange.com/questions/195348/implement-the-max-pooling-operation-from-convolutional-neural-networks

So What?

Today CNNs are being used in autonomous vehicles along with lasers to classify object around them. There are many applications in healthcare imaging, but most CNNs only classify “does it have this disease or not” and therefore still has some future improvements. Microsoft AI is constantly capturing photos of habitats of endangered species and recording data. The possibilities are endless from effecting genetics to flying us around the world when you give computer vision.

TL;DR

  1. We don’t have perfect vision
  2. Computers can use machine learning and convolution neural networks to see images
  3. Computer vision is possible by taking pixel values and applying kernel filters
  4. CNNs have endless possibilities to affect areas like healthcare and genetics to autonomous vehicles

Before You Go

  • You can get in contact with me via Linden or email at adamomarali37@gmail.com
  • You can also check out my YouTube channel where I will be uploading content weekly on interesting topics like CNNs
  • And be sure to give your boy a clap if you learned something

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