Detection of breast cancer using machine learning on time-series diffuse optical transillumination data.

ABSTRACT:

Significance

Optical mammography as a promising tool for cancer diagnosis has largely fallen behind expectations. Modern machine learning (ML) methods offer ways to improve cancer detection in diffuse optical transmission data.

Aim

We aim to quantitatively evaluate the classification of cancer-positive versus cancer-negative patients using ML methods on raw transmission time series data from bilateral breast scans during subjects' rest.

Approach

We use a support vector machine (SVM) with hyperparameter optimization and cross-validation to systematically explore a range of data preprocessing and feature-generation strategies. We also apply an automated ML (AutoML) framework to validate our findings. We use receiver operating characteristics and the corresponding area u

SUBMITTER: Harnischmacher N 

PROVIDER: S-EPMC11552526 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature