Google Cloud Platform Blog
Product updates, customer stories, and tips and tricks on Google Cloud Platform
Neural Network for Breast Cancer Data Built on Google App Engine
Tuesday, August 7, 2012
Today’s guest blog post comes from 17-year-old
Brittany Wenger, the winner of this year’s
Google Science Fair
. Brittany built an application on Google App Engine called the "
Global Neural Network Cloud Service for Breast Cancer." This artificial neural network can detect complex patterns in data, learning how to classify malignant or cancerous cells it hasn’t seen before.
Learn more about her project
.
When a patient has a palpable breast lump, the first step a doctor takes is to determine whether the mass is malignant or benign. One relatively simple diagnostic procedure is a form of biopsy called fine needle aspiration (FNA). Though these tests are less invasive than others, they are historically less accurate as well. My goal was to create a tool for doctors to use when interpreting test results from these procedures.
For this project, I decided to create a neural network built on Google App Engine, using
data
published to the
Machine Learning Repository
by the University of Wisconsin. A neural network attempts to replicate the brain as a form of artificial intelligence through networks of computers and can be used to detect extremely complex patterns. It learns from its mistakes, so it can classify a case it hasn’t seen before as malignant or cancerous based on specific criteria like clump thickness or bland chromatin. Because the diagnostic power of the network improves the more data it has, building on App Engine is a way to ensure the app can continue to scale easily, no matter how much information goes into the system.
I got started integrating my neural network application code, written in Java, with App Engine in a few hours using the SDK’s Greeting Service sample code as a starting point. The application has two main parts, a training module, that implements the neural network itself and runs the training process over the input data stored in static files, and a web interface that takes input data and returns the network’s analysis.
Google App Engine provides the scalable infrastructure I need to collect information from every hospital in the world and run when there are many concurrent requests, as usage of my application increases. Because my network is built as a cloud service, not only is my app working on the web, but mobile tablets, smartphones, old PC systems, or new technologies can also easily access the service from any hospital with an internet connection.
The neural network I developed is 99.11% sensitive to malignancy when using
leave-one-out testing
with original data.
Thus far, I have run 7.6 million trials. Moving forward my goal is to make the application accessible to the global medical community so more data can be deposited and used to improve the diagnostic power of the network.
Free Trial
GCP Blogs
Big Data & Machine Learning
Kubernetes
GCP Japan Blog
Firebase Blog
Apigee Blog
Popular Posts
Understanding Cloud Pricing
World's largest event dataset now publicly available in BigQuery
A look inside Google’s Data Center Networks
New in Google Cloud Storage: auto-delete, regional buckets and faster uploads
Getting your data on, and off, of Google App Engine
Labels
Announcements
193
Big Data & Machine Learning
134
Compute
271
Containers & Kubernetes
92
CRE
27
Customers
107
Developer Tools & Insights
151
Events
38
Infrastructure
44
Management Tools
87
Networking
43
Open
1
Open Source
135
Partners
102
Pricing
28
Security & Identity
85
Solutions
24
Stackdriver
24
Storage & Databases
164
Weekly Roundups
20
Feed
Subscribe by email
Demonstrate your proficiency to design, build and manage solutions on Google Cloud Platform.
Learn More
Technical questions? Check us out on
Stack Overflow
.
Subscribe to
our monthly newsletter
.
Google
on
Follow @googlecloud
Follow
Follow