Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network

Abstract

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

The contribution of hippocampal subfields to the progression of neurodegeneration

“…Overall our proposed deep learning-based classification framework corroborates earlier results, by demonstrating comparable accuracy performance to those reported previously for the classification of pMCI vs. sMCI (16)(17)(18). It demonstrates that whole-hippocampus structural features can be used to differentiate pMCI from sMCI.…”

“…The size of the 3D bounding box for each hippocampus was 44 x 52 x 52 voxels. As in previous studies (16,18) the deep learning model was trained to first differentiate the AD and CN groups and then tested on the task of differentiating the pMCI and sMCI groups. Data augmentation was applied within the training data set to improve the performance of the model and its generalizability.…”

“…Studies have also implicated the hippocampus in the progression of MCI (15). Of particular interest, a series of recent studies have utilized deep learning to differentiate progressive and stable MCI (16,17) or predict individual subjects' progression from MCI to AD (18) based on whole hippocampus structural features. However, rather than being a homogeneous structure, the hippocampus is complex and heterogeneous (19).…”

Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging

“…Convolutional neural network (CNN) is one of the most representative network models in the field of deep learning. As a research hotspot in current image processing, CNN has achieved tremendous success and wide application in image recognition and classification [21,22]. Therefore, we applied CNN to endoscopic diagnosis in an attempt to further improve diagnostic efficacy of early gastric cancer.…”

Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems

“…The experimented objective was a sequential data classification and several Gated Recurrent Unit (GRU) for each data modality were trained and adopted for MCI prediction. Several prior works Zhang et al [21], Liu et al [22], Samper-González et al [23] and Huang et al [24] apply machine learning and neuroimaging to distinguish between cognitively unimpaired controls and patients with MCI and AD. A traditional way is to first extract features like volume, cortical thickness or gray matter volume from neuroimaging and then perform feature selection, as well as dimension and noise reduction.…”