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

Andrew A. Borkowski; Catherine P. Wilson; Steven A. Borkowski; Lauren A. Deland; Stephen M. Mastorides

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

Quantitative Methods 2019-01-23 v2 Machine Learning Machine Learning

Authors: Andrew A. Borkowski , Catherine P. Wilson , Steven A. Borkowski , Lauren A. Deland , Stephen M. Mastorides

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.

Keywords

cancer biology biomedical image analysis machine learning in genomics

Cite

@article{arxiv.1808.08230,
  title  = {Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer},
  author = {Andrew A. Borkowski and Catherine P. Wilson and Steven A. Borkowski and Lauren A. Deland and Stephen M. Mastorides},
  journal= {arXiv preprint arXiv:1808.08230},
  year   = {2019}
}

Comments

12 pages, 2 tables, 3 figures

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

Abstract

Keywords

Cite

Comments

Related papers