access icon free 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 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.

Inspec keywords: biological tissues; learning (artificial intelligence); image classification; liver; biomedical MRI; medical image processing; diseases; brain

Other keywords: brain; optimised deep transfer learning approach; machine learning paradigm; AUC pairs; liver; random forest; Visual Geometric Group-19; ML-based soft classifier; computer-aided design-based automated classification strategy; four-fold augmentation; MRI scans; brain MRI-based Wilson disease tissue classification; VGG-19; MobileNet; white matter hyperintensity

Subjects: Patient diagnostic methods and instrumentation; Computer vision and image processing techniques; Medical magnetic resonance imaging and spectroscopy; Knowledge engineering techniques; Image recognition; Biology and medical computing; Biomedical magnetic resonance imaging and spectroscopy

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