Generalised deep learning framework for HEp-2 cell recognition using local binary pattern maps

• The authors propose a novel HEp-2 cell image classifier to improve the automation process of patients' serum evaluation. The authors' solution builds on the recent progress in deep learning based image classification. They propose an ensemble approach using multiple state-of-the-art architectures. They incorporate additional texture information extracted by an improved version of local binary patterns maps, $\alpha $alpha LBP-maps, which enables to create a very effective cell image classifier. This innovative combination is trained on three publicly available datasets and its general applicability is demonstrated through the evaluation on three independent test sets. The presented results show that their approach leads to a general improvement of performance on average on the three public datasets.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

medical image processing

learning (artificial intelligence)

image texture

image classification

biological techniques

deep learning based image classification

ensemble approach

texture information

local binary patterns maps

effective cell image classifier

generalised deep learning framework

HEp-2 cell recognition

HEp-2 cell image classifier

local binary pattern mapping

serum evaluation

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)