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

TL;DR

This study demonstrates that Apple Create ML can accurately detect and subclassify non-small cell lung cancer from histopathological images, potentially enabling smartphone-based diagnostics to improve healthcare access.

Contribution

First application of Apple Create ML for lung cancer detection and subclassification using histopathological images, showing high accuracy and potential for mobile diagnostics.

Findings

100% detection accuracy for non-small cell lung cancer images

Successful subclassification of most images

Potential for smartphone-based diagnostic applications

Abstract

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.