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

L. Saba; M. Agarwal; S.S. Sanagala; S.K. Gupta; G.R. Sinha; A.M. Johri; N.N. Khanna; S. Mavrogeni; J.R. Laird; G. Pareek; M. Miner; P.P. Sfikakis; A. Protogerou; V. Viswanathan; G.D. Kitas; J.S. Suri

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

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Author(s): L. Saba¹ ; M. Agarwal² ; S.S. Sanagala³ ; S.K. Gupta² ; G.R. Sinha⁴ ; A.M. Johri⁵ ; N.N. Khanna⁶ ; S. Mavrogeni⁷ ; J.R. Laird⁸ ; G. Pareek⁹ ; M. Miner¹⁰ ; P.P. Sfikakis¹¹ ; A. Protogerou¹² ; V. Viswanathan¹³ ; G.D. Kitas¹⁴ ; J.S. Suri¹⁵
- Affiliations: 1: Department of Radiology , Azienda Ospedaliero Universitaria (A.O.U.) , Cagliari , Italy ;
  2: CSE Department , Bennett University , Greater Noida, UP , India ;
  3: Department CSE , CMR College of Engineering & Technology , Hyderabad , India ;
  4: Myanmar Institute of Information Technology (MIIT), Mandalay , Myanmar ;
  5: Department of Medicine, Division of Cardiology , Queen's University , Kingston, Ontario , Canada ;
  6: Department of Cardiology , Indraprastha APOLLO Hospitals , New Delhi , India ;
  7: Cardiology Clinic , Onassis Cardiac Surgery Center , Athens , Greece ;
  8: Heart and Vascular Institute , Adventist Health St. Helena, St Helena, CA , USA ;
  9: Minimally Invasive Urology Institute, Brown University , Providence, Rhode Island , USA ;
  10: Men's Health Center, Miriam Hospital Providence , Rhode Island , USA ;
  11: Rheumatology Unit , National Kapodistrian University of Athens , Athens , Greece ;
  12: Department of Cardiovascular Prevention , National and Kapodistrian University of Athens , Athens , Greece ;
  13: MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre , Chennai , India ;
  14: R & D Academic Affairs , Dudley Group NHS Foundation Trust , Dudley , United Kingdom ;
  15: Stroke Monitoring and Diagnosis Division , AtheroPoint™ , Roseville, CA , USA
Source: Volume 56, Issue 25, 10 December 2020, p. 1395 – 1398
DOI: 10.1049/el.2020.2102 , Print ISSN 0013-5194, Online ISSN 1350-911X

Received 16/07/2020, Published 04/11/2020

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.

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

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