Hemorrhage Evaluation and Detector System for Underserved Populations: HEADS-UP.

ABSTRACT:

Objective

To create a rapid, cloud-based, and deployable machine learning (ML) method named hemorrhage evaluation and detector system for underserved populations, potentially across the Mayo Clinic enterprise, then expand to involve underserved areas and detect the 5 subtypes of intracranial hemorrhage (IH).

Methods

We used Radiological Society of North America dataset for IH detection. We made 4 total iterations using Google Cloud Vertex AutoML. We trained an AutoML model with 2000 images, followed by 6000 images from both IH positive and negative classes. Pixel values were measured by the Hounsfield units, presenting a width of 80 Hounsfield and a level of 40 Hounsfield as the bone window. This was followed by a more detailed image preprocessing approach by combining the p

SUBMITTER: Salman S 

PROVIDER: S-EPMC11975646 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature