Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.00509/full |
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| author | Yechong Huang Jiahang Xu Yuncheng Zhou Tong Tong Xiahai Zhuang the Alzheimer’s Disease Neuroimaging Initiative (ADNI) |
| author_facet | Yechong Huang Jiahang Xu Yuncheng Zhou Tong Tong Xiahai Zhuang the Alzheimer’s Disease Neuroimaging Initiative (ADNI) |
| author_sort | Yechong Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T04:38:30Z |
| format | Article |
| id | doaj.art-57a6d42626ef4275a36230c2d75fd1a4 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T04:38:30Z |
| publishDate | 2019-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-57a6d42626ef4275a36230c2d75fd1a42022-12-22T02:01:57ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-05-011310.3389/fnins.2019.00509448373Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural NetworkYechong Huang0Jiahang Xu1Yuncheng Zhou2Tong Tong3Xiahai Zhuang4the Alzheimer’s Disease Neuroimaging Initiative (ADNI)School of Data Science, Fudan University, Shanghai, ChinaSchool of Data Science, Fudan University, Shanghai, ChinaSchool of Data Science, Fudan University, Shanghai, ChinaFujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou, ChinaSchool of Data Science, Fudan University, Shanghai, ChinaAlzheimer’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.https://www.frontiersin.org/article/10.3389/fnins.2019.00509/fullAlzheimer’s diseasemulti-modalityimage classificationCNNdeep learninghippocampal |
| spellingShingle | Yechong Huang Jiahang Xu Yuncheng Zhou Tong Tong Xiahai Zhuang the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network Frontiers in Neuroscience Alzheimer’s disease multi-modality image classification CNN deep learning hippocampal |
| title | Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network |
| title_full | Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network |
| title_fullStr | Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network |
| title_full_unstemmed | Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network |
| title_short | Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network |
| title_sort | diagnosis of alzheimer s disease via multi modality 3d convolutional neural network |
| topic | Alzheimer’s disease multi-modality image classification CNN deep learning hippocampal |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.00509/full |
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