Brain MRI-based Wilson disease tissue classification: an optimised deep transfer learning approach

Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visual...

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Bibliographic Details
Published in:	Electronics letters Vol. 56; no. 25; pp. 1395 - 1398
Main Authors:	Saba, L, Agarwal, M, Sanagala, S.S, Gupta, S.K, Sinha, G.R, Johri, A.M, Khanna, N.N, Mavrogeni, S, Laird, J.R, Pareek, G, Miner, M, Sfikakis, P.P, Protogerou, A, Viswanathan, V, Kitas, G.D, Suri, J.S
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 10.12.2020
Subjects:	AUC pairs biological tissues biomedical MRI brain brain MRI‐based Wilson disease tissue classification computer‐aided design‐based automated classification strategy diseases four‐fold augmentation image classification learning (artificial intelligence) liver machine learning paradigm medical image processing ML‐based soft classifier MobileNet MRI scans optimised deep transfer learning approach random forest Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications VGG‐19 Visual Geometric Group‐19 white matter hyperintensity MRI scans biomedical MRI brain MRI-based Wilson disease tissue classification Visual Geometric Group-19 image classification liver biological tissues diseases random forest brain optimised deep transfer learning approach machine learning paradigm computer-aided design-based automated classification strategy white matter hyperintensity AUC pairs MobileNet VGG-19 learning (artificial intelligence) four-fold augmentation ML-based soft classifier medical image processing
ISSN:	0013-5194, 1350-911X, 1350-911X
Online Access:	Get full text
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Summary:	Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer-aided design-based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group-19 (VGG-19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four-fold augmentation, VGG-19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70%, 0.932 (p < 0.0001) and 86.87 ± 2.23%, 0.871 (p < 0.0001), respectively. Further, MobileNet and VGG-19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML-based soft classifier – Random Forest.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2020.2102