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

Content Provider	IET Digital Library
Author	Bajić, Buda Majtner, Tomáš Lindblad, Joakim Sladoje, Nataša
Abstract	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, α 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.
Starting Page	1201
Ending Page	1208
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 6, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0705
Journal	IET Image Processing
Publisher Date	2020-01-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biological Technique Biology And Medical Computing Biomedical Measurement And Imaging Biophysical Instrumentation And Technique Computer Vision And Image Processing Technique Deep Learning Based Image Classification Effective Cell Image Classifier Ensemble Approach Generalised Deep Learning Framework Health Physics HEp-2 Cell Image Classifier HEp-2 Cell Recognition Image Classification Image Recognition Image Texture Knowledge Engineering Technique Learning in AI Local Binary Pattern Mapping Local Binary Pattern Maps Medical And Biomedical Uses of Field Medical Image Processing Patient Diagnostic Method And Instrumentation Radiations Radioactivity Serum Evaluation Texture Information
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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