Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer
| Title: | Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer |
|---|---|
| Authors: | Borkowski, Andrew A., Wilson, Catherine P., Borkowski, Steven A., Deland, Lauren A., Mastorides, Stephen M. |
| Publication Year: | 2018 |
| Collection: | Computer Science Quantitative Biology Statistics |
| Subject Terms: | Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Statistics - Machine Learning |
| More Details: | 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. Comment: 12 pages, 2 tables, 3 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/1808.08230 |
| Accession Number: | edsarx.1808.08230 |
| Database: | arXiv |
| Description not available. |