Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks.

ABSTRACT: The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basi

SUBMITTER: Pelka O 

PROVIDER: S-EPMC6231616 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature