Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer

Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose bu...

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Bibliographic Details
Published in:arXiv.org 2019-01
Main Authors: Borkowski, Andrew A, Wilson, Catherine P, Borkowski, Steven A, Deland, Lauren A, Mastorides, Stephen M
Format: Article
Language:English
Subjects:
Cancer
Diagnostic software
Diagnostic systems
Image classification
Image detection
Lung cancer
Machine learning
Medical imaging
Modules
Smartphones
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Summary:Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify non-small cell lung cancer. In our study, we evaluated the utility of using Apple Create ML module to detect and sub-classify non-small cell carcinomas based on histopathological images. After module optimization, the program detected 100% of non-small cell lung cancer images and successfully subclassified the majority of the images. Trained modules, such as ours, can be utilized in diagnostic smartphone-based applications, augmenting diagnostic services in understaffed areas of the world.
ISSN:2331-8422