Abstract
Wilson’s disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a “conventional deep convolution neural network” (cDCNN) and an “improved DCNN” (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring “differentiable at zero.” Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning–based “Inception V3” paradigm by 11.92% and (b) four types of “conventional machine learning–based systems”: k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis.
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Appendix
If TP, FP, TN, FN, TPR, and FPR represent true positive, false positive, true negative, false negative, true-positive rate, and false-positive rate, respectively, then the performance parameters can be computed as follows:
where yi is the class label for input and pi is the predicted probability of class being yi.
where σ is the standard deviation and xi is the accuracy at ith combination of K-fold cross-validation, μ is the mean accuracy, and N is the number of combinations, equal to 10 for K10.
Scientific validation of DCNN systems
The DCNN9*-Augm4* system was validated using well-accepted and well-published facial biometric data. The facial dataset consisted of 72 subjects, and each subject represented a class. Each subject had 20 different face images totaling to 1440 images in the dataset. Using K10 protocol, the accuracy obtained with cDCNN, tDCNN, and iDCNN was 96.72 ± 2.01%, 97.18 ± 1.23%, and 98.27 ± 1.55%, respectively. These numbers are comparable with the accuracy obtained in WD. The order of performance was iDCNN > tDCNN > cDCNN. The results demonstrated the proposed DCNN methods as they promised encouraging high accuracy on an already published dataset. Table 11 shows the 10 K10 combinations of cDCNN, tDCNN, and iDCNN for the facial biometric dataset in sorted form.
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Agarwal, M., Saba, L., Gupta, S.K. et al. Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application. Med Biol Eng Comput 59, 511–533 (2021). https://doi.org/10.1007/s11517-021-02322-0
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DOI: https://doi.org/10.1007/s11517-021-02322-0