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Google Cloud AutoML Vision for Medical Image Classification

Pneumonia Detection using Chest X-Ray Images

Ekaba Bisong
Towards Data Science
7 min readMay 10, 2019

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The normal chest X-ray (left panel) shows clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse “interstitial” pattern in both lungs. (Source: Kermany, D. S., Goldbaum M., et al. 2018. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5)

Google Cloud AutoML Vision simplifies the creation of custom vision models for image recognition use-cases. The concepts of neural architecture search and transfer learning are used under the hood to find the best network architecture and the optimal hyperparameter configuration that minimizes the loss function of the model. This article uses Google Cloud AutoML Vision to develop an end-to-end medical image classification model for Pneumonia Detection using Chest X-Ray Images.

Table of Contents

About the Dataset

The dataset contains:

  • 5,232 chest X-ray images from children.
  • 3,883 of those images are samples of bacterial (2,538) and viral…

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Ekaba Bisong
Ekaba Bisong

Written by Ekaba Bisong

AI Researcher, Google Developer Expert in Machine Learning and author of book “Building Machine Learning and Deep Learning Models on Google Cloud Platform”

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