Brain MRI‐based Wilson disease tissue classification: an optimised deep transfer learning approach
Year: 2020
Type: article
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.
Source: Electronics Letters
DOI: 10.1049/el.2020.2102
Institutions: Azienda Ospedaliero-Universitaria Cagliari, Bennett University, Myanmar Institute of Information Technology, Queen's University, Indraprastha Apollo Hospitals, Onassis Cardiac Surgery Center, St. Helena Hospital, Adventist Heart Institute, Brown University, Miriam Hospital, National and Kapodistrian University of Athens, M.V. Hospital for Diabetes and Diabetes Research Centre, Dudley Group NHS Foundation Trust
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3097180705