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Lookup NU author(s): Dr Deepayan BhowmikORCiD
This is the of a conference proceedings (inc. abstract) that has been published in its final definitive form by SPIE, 2023.
For re-use rights please refer to the publisher's terms and conditions.
© 2023 SPIE. Measuring hyperplasia in Atlantic salmon gills can give important insight into fish health and environmental conditions such as water quality. This paper proposes a novel histology image classification technique to identify hyperplastic regions using an emerging signal decomposition technique, Empirical Wavelet Transform (EWT) in combination with a fully connected neural network (FCNN). Due to its adaptive nature, we hypothesise and show that EWT effectively represents unique features of gill histopathology whole slide images that help in the classification task. Our hybrid approach is unique and significantly outperformed regular deep learning-based methods considering a joint speed-accuracy metric.
Author(s): Carmichael AFB, Baily JL, Reeves A, Ochoa G, Boerlage AS, Gunn G, Allshire R, Bhowmik D
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Medical Imaging 2023: Digital and Computational Pathology
Year of Conference: 2023
Online publication date: 06/04/2023
Acceptance date: 02/04/2018
Date deposited: 02/02/2023
ePrints DOI: 10.57711/ph8p-p112
Library holdings: Search Newcastle University Library for this item
Series Title: SPIE Conference Proceedings