Machine Learning with TensorFlow for the Average Joe

I attempted to retrain the final layer of the Inception v3 model against images of chest X-rays with and without signs of tuberculosis. Or in plain English, I wanted to see if I could diagnose tuberculosis with machine learning!

The beauty of this exercise is that it doesn’t require a great deal of programming knowledge or machine learning expertise.

The Inception model is a convolutional neural network. If that sounds confusing, just understand that the model can detect features of images and classify new images based on those features. A feature is an observation of the data. Humans have unique features, which is why we can tell a human is a human, or a dog is a dog, or a pizza is a pizza. Using methods such as shape detection, corner detection, and blob detection the model can identify features of images. For example, if enough pizza images seen by the model are round, the model may begin to associate roundedness with pizzas and apply that knowledge to any new image it encounters.

Inception is trained on a vast image database known as ImageNet. ImageNet images are pre-classified so that identified features can be attributed to those classes. We can use a technique called transfer learning to retrain the inception model with any class of images we want, in this case chest X-rays with and without tuberculosis.

These concepts become easier to understand as we go through the exercise.

I downloaded the Shenzhen images and converted them all from pngs to JPEG format. I separated the abnormal and normal images into two folders (abnormal images end in 1, normal images end in 0). Then I put the abnormal and normal folders into a parent folder called lungs. I took out 50 images from the dataset (25 abnormal and 25 normal) for testing later on.

Once I had TensorFlow installed and the images prepared I proceeded to retrain the model.

First, I built the retrainer from within the TensorFlow source directory. This took just over an hour on my four year old Mac. I did it in terminal with the following commands

$ cd tensorflow

Then I ran the retrainer against my chest X-ray images.

The line/Users/hali/lungs depends on where the images are stored.

Here we are using transfer learning. It allows us to leverage the knowledge of the pre-trained Inception model (trained against the ImageNet database) and apply it to our labelled dataset.

First, bottlenecks are calculated. Bottlenecks are values for each image in the dataset that are cached and later fed to the classification layer. The bottlenecks are meant to be meaningful and compact pieces of information that the classification layer can use to distinguish the image quickly and accurately. Without pre-caching bottlenecks the retraining process would take significantly longer.

Retraining is broken down into steps. In each step 10 images are randomly chosen from a training set. Their bottlenecks are then fed to the final layer of the model and used to predict their classification. Whether the classifier was right or wrong will then impact the weights given to the nodes in the model. Within each step 3 values are calculated — training accuracy, cross entropy, and validation accuracy.

The following is an example output of a training step.

Step 1750: Train accuracy = 95.0%

Step 1750: Cross entropy = 0.205942

Step 1750: Validation accuracy = 82.0% (N=100)

TensorFlow will spit out a final test accuracy for the model.

Final test accuracy = 84.1% (N=69)

To test, I entered the following into terminal

After a few moments TensorFlow spit out the following

Here the model states that the image I presented was more similar to an abnormal chest X-ray than a normal one. It also states the relative strength of that prediction. For this particular image, you could say the model was relatively confident with the prediction (~97%).

I tested my dataset with 3 different parameter settings, focusing solely on training steps and learning rate. I was able to achieve slightly different results with different parameters. Later, I found I was also able to improve accuracy by using higher quality JPEG images.

By looking strictly at whether my model was right or wrong, (not considering strength of association) my model went from being correct 60% of the time, to 74% of the time, to 78% of the time. This was done through increasing the number of training steps, playing around with the learning rate, and removing hard-to-classify images from the dataset.

At this point I was pleased with myself for having a functioning model with decent results. However, I wasn’t aware of how my model was differentiating between the images, or why it was correctly classifying some images and not others. I discovered that my dataset was small, all of my images came from one hospital, the population they came from was the same, and the way in which the pictures were presented was always the same. These factors directly impact the generalizability of the model. That is to say, the model may only be effective for the particular dataset that I have presented.

Some ways to increase the generalizability of the model would be to increase the size of the dataset, add more variance to the images, play with hyperparameters, add X-rays presented in different ways, and add noise to the images. Humans come in all shapes and sizes, it’s important to include images that represent as many variations as possible in order to make the model more robust.

I only retrained the model on chest X-rays. So what would happen if I present it something completely different?

One would suspect that an image of a cow would not be associated with either abnormal or normal chest X-rays, however the model stated differently…

In fact, the model presents a near 100% association to normal chest X-rays. What features could possibly exist within this cow image that are so strongly associated with features of normal chest X-rays? To answer that question is my next task. I look forward to exploring the mechanism by which the model is able to detect features, and how it uses those features in classification.

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