An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging
Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/740 |
| _version_ | 1827658900039532544 |
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| author | Modupe Odusami Rytis Maskeliūnas Robertas Damaševičius |
| author_facet | Modupe Odusami Rytis Maskeliūnas Robertas Damaševičius |
| author_sort | Modupe Odusami |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD. |
| first_indexed | 2024-03-09T23:11:05Z |
| format | Article |
| id | doaj.art-c11faef88475406895c990472902b103 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:11:05Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c11faef88475406895c990472902b1032023-11-23T17:44:30ZengMDPI AGSensors1424-82202022-01-0122374010.3390/s22030740An Intelligent System for Early Recognition of Alzheimer’s Disease Using NeuroimagingModupe Odusami0Rytis Maskeliūnas1Robertas Damaševičius2Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, LithuaniaDepartment of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, LithuaniaDepartment of Software Engineering, Kaunas University of Technology, 51368 Kaunas, LithuaniaAlzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.https://www.mdpi.com/1424-8220/22/3/740intelligent systemsimage processingexpert systemsAlzheimer’s diseaseMRIdeep learning |
| spellingShingle | Modupe Odusami Rytis Maskeliūnas Robertas Damaševičius An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging Sensors intelligent systems image processing expert systems Alzheimer’s disease MRI deep learning |
| title | An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging |
| title_full | An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging |
| title_fullStr | An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging |
| title_full_unstemmed | An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging |
| title_short | An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging |
| title_sort | intelligent system for early recognition of alzheimer s disease using neuroimaging |
| topic | intelligent systems image processing expert systems Alzheimer’s disease MRI deep learning |
| url | https://www.mdpi.com/1424-8220/22/3/740 |
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