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

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Title: Brain MRI-based Wilson disease tissue classification: an optimised deep transfer learning approach.
Authors: Saba, L.1, Agarwal, M.2, Sanagala, S. S.3, Gupta, S. K.2, Sinha, G. R.4, Johri, A. M.5, Khanna, N. N.6, Mavrogeni, S.7, Laird, J. R.8, Pareek, G.9, Miner, M.10, Sfikakis, P. P.11, Protogerou, A.12, Viswanathan, V.13, Kitas, G. D.14, Suri, J. S.15 Jasjit.Suri@AtheroPoint.com
Source: Electronics Letters (Wiley-Blackwell). 12/10/2020, Vol. 56 Issue 25, p1395-1398. 3p.
Subjects: Deep learning, Nosology, Hepatolenticular degeneration, White matter (Nerve tissue), Random forest algorithms
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:00135194
DOI:10.1049/el.2020.2102