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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| Published in: | Electronics letters Vol. 56; no. 25; pp. 1395 - 1398 |
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| Main Authors: | , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
10.12.2020
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| ISSN: | 0013-5194, 1350-911X, 1350-911X |
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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. |
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| AbstractList | 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. |
| Author | Sfikakis, P.P Mavrogeni, S Protogerou, A Khanna, N.N Laird, J.R Sinha, G.R Suri, J.S Agarwal, M Gupta, S.K Kitas, G.D Sanagala, S.S Saba, L Viswanathan, V Miner, M Pareek, G Johri, A.M |
| Author_xml | – sequence: 1 givenname: L surname: Saba fullname: Saba, L organization: Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy – sequence: 2 givenname: M surname: Agarwal fullname: Agarwal, M organization: CSE Department, Bennett University, Greater Noida, UP, India – sequence: 3 givenname: S.S surname: Sanagala fullname: Sanagala, S.S organization: Department CSE, CMR College of Engineering & Technology, Hyderabad, India – sequence: 4 givenname: S.K surname: Gupta fullname: Gupta, S.K organization: CSE Department, Bennett University, Greater Noida, UP, India – sequence: 5 givenname: G.R surname: Sinha fullname: Sinha, G.R organization: Myanmar Institute of Information Technology (MIIT), Mandalay, Myanmar – sequence: 6 givenname: A.M surname: Johri fullname: Johri, A.M organization: Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada – sequence: 7 givenname: N.N surname: Khanna fullname: Khanna, N.N organization: Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India – sequence: 8 givenname: S surname: Mavrogeni fullname: Mavrogeni, S organization: Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece – sequence: 9 givenname: J.R surname: Laird fullname: Laird, J.R organization: Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA – sequence: 10 givenname: G surname: Pareek fullname: Pareek, G organization: Minimally Invasive Urology Institute, Brown University, Providence, Rhode Island, USA – sequence: 11 givenname: M surname: Miner fullname: Miner, M organization: Men's Health Center, Miriam Hospital Providence, Rhode Island, USA – sequence: 12 givenname: P.P surname: Sfikakis fullname: Sfikakis, P.P organization: Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece – sequence: 13 givenname: A surname: Protogerou fullname: Protogerou, A organization: Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, Athens, Greece – sequence: 14 givenname: V surname: Viswanathan fullname: Viswanathan, V organization: MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India – sequence: 15 givenname: G.D surname: Kitas fullname: Kitas, G.D organization: R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, United Kingdom – sequence: 16 givenname: J.S surname: Suri fullname: Suri, J.S email: Jasjit.Suri@AtheroPoint.com organization: Stroke Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA, USA |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Brain MRI-based Wilson disease tissue classification: an optimised deep transfer learning approach |
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