About this document
Sensors: A Shallow Convolutional Learning Network For Classification of Cancers Based On Copy Number Variations by Danielle Santana is a document available to read on EtoBox.
The document discusses the development of a shallow convolutional learning network for classifying cancer types based on genomic copy number variations (CNVs). It reviews existing machine learning methods and proposes three deep learning techniques, achieving high classification accuracy, particularly with the ResCNN6 model. The study emphasizes the importance of CNVs in cancer diagnosis and treatment, highlighting challenges in classification due to feature overlap among certain cancer types.
- Author
- Danielle Santana
- Language
- EN