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Deep versus conventional machine learning for MRI‐based diagnosis and prediction of Alzheimer’s disease
Author(s) -
Bron Esther E.,
Venkatraghavan Vikram,
Linders Jara,
Niessen Wiro J.,
Klein Stefan
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.040957
Subject(s) - softmax function , artificial intelligence , support vector machine , pattern recognition (psychology) , convolutional neural network , neuroimaging , computer science , cross validation , machine learning , test set , classifier (uml) , deep learning , random forest , alzheimer's disease neuroimaging initiative , dementia , medicine , disease , pathology , psychiatry
Background Accurate diagnosis and prediction of Alzheimer's disease (AD) is important for care planning and clinical trials. Machine learning based on MRI showed promising results for these tasks. While in many medical imaging applications deep neural networks have outperformed conventional approaches, this has not been shown for AD classification. Therefore, this work presents a direct comparison of conventional and deep AD classification. Methods We included participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): 334 AD patients, 520 cognitively normals (CN), 231 mild cognitive impaired (MCI) patients who converted to AD within 3 years (MCIc) and 628 MCI patients who did not convert (MCInc). As input features we considered two MRI‐based images in MNI‐space: raw T1‐weighted images and preprocessed modulated GM maps that encode gray matter density. As conventional classifier, we used a linear support vector machine (SVM). The c‐parameter was optimized with 5‐fold crossvalidation on the training set. The deep classifier was an all convolutional neural network (all‐CNN) that consisted of 7 convolutional blocks and a final softmax classification layer (Fig. 1). A validation set (10%) was used for regularization by early stopping. Classifiers were trained for AD‐CN classification using 20 repetitions of random split (10% test). Confidence intervals (95%CI) on performance measures were computed with the corrected resampled t‐test. Based on AD‐CN results, we selected the best input feature and retrained classifiers on the full AD/CN dataset to evaluate predictive performance (MCIc‐MCInc). Results For AD‐CN classification based on modulated GM maps, the AUC for SVM (0.940; 95%CI: 0.924‐0.955) was similar to that of all‐CNN (0.933; 95%CI: 0.918‐0.948) (Fig. 2). Both with SVM and all‐CNN, classification based on modulated GM maps outperformed classification based on T1w‐images, especially in the case of SVM. Application to MCI conversion prediction (Fig. 3) yielded slightly higher performance for SVM (AUC=0.765) than for all‐CNN (AUC=0.742) (p=0.01 for McNemar’s test). Conclusion Deep and conventional classifiers performed equally well in AD detection. For AD prediction, the conventional classifier yielded slightly higher performance. Both approaches benefitted from the use of modulated GM maps instead of raw T1‐weighted images, while this effect is stronger for the conventional method.