Pneumonia Classification on X-rays using TensorFlow and Tensor Processing Units (TPUs) | Kaggle

TIMESTAMPS
0:00 Introduction
0:23 Load the data
0:55 Visualize the dataset
1:31 Build the CNN (convolutional neural network)
3:55 Correct for data imbalance
5:52 Fine tune the model
7:08 Visualizing model performance
7:29 Predict and evaluate results

Machine learning has a phenomenal range of applications, including in health and diagnostics. This tutorial will explain the complete pipeline from loading data to predicting results, and it will explain how to build an X-ray image classification model from scratch to predict whether an X-ray scan shows presence of pneumonia.

This tutorial will explain how to utilize TPUs efficiently, load in image data, build and train a convolution neural network, fine tune and regularize the model, and predict results. Data augmentation is not included in the model because X-ray scans are only taken in a specific orientation, and variations such as flips and rotations will not exist in real X-ray images.

You can follow along in Amy's notebook here: https://www.kaggle.com/amyjang/tensorflow-pneumonia-classification-on-x-rays

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