A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images
AbstractAlzheimer’s disease is a gradual neurodegenerative condition affecting the brain, causing a decline in cognitive function by progressively damaging nerve cells over time. While a cure for Alzheimer’s remains elusive, the detection of Alzheimer’s disease (AD) through brain biomarkers is cruci...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2024.2314872 |
| _version_ | 1797316737026228224 |
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| author | Nitika Goenka Akhilesh Kumar Sharma Shamik Tiwari Nagendra Singh Vyom Yadav Srikanth Prabhu Krishnaraj Chadaga |
| author_facet | Nitika Goenka Akhilesh Kumar Sharma Shamik Tiwari Nagendra Singh Vyom Yadav Srikanth Prabhu Krishnaraj Chadaga |
| author_sort | Nitika Goenka |
| collection | DOAJ |
| description | AbstractAlzheimer’s disease is a gradual neurodegenerative condition affecting the brain, causing a decline in cognitive function by progressively damaging nerve cells over time. While a cure for Alzheimer’s remains elusive, the detection of Alzheimer’s disease (AD) through brain biomarkers is crucial to impede its advancement. High-resolution structural MRI scans, particularly T1-weighted images, are commonly used in Alzheimer’s detection. These images provide detailed information about the brain’s structure, allowing researchers and clinicians to identify abnormalities. Our study employs a deep learning methodology using T1-weighted MRI images for a binary classification task—distinguishing between AD and normal/healthy control (NC). The volumetric convolutional neural network model is deployed on pre-processed images and validated on MIRIAD datasets, achieving an impressive accuracy of 97%, surpassing other network models. Addressing the challenge of limited datasets for deep learning models, we incorporated various augmentation techniques such as rotation and rescaling, resulting in outstanding model accuracy and effective discerning between Alzheimer’s disease and normal controls. |
| first_indexed | 2024-03-08T03:23:39Z |
| format | Article |
| id | doaj.art-9286fe3351c14527b5a4eed72f1deced |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-08T03:23:39Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-9286fe3351c14527b5a4eed72f1deced2024-02-12T07:25:48ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2024.2314872A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI imagesNitika Goenka0Akhilesh Kumar Sharma1Shamik Tiwari2Nagendra Singh3Vyom Yadav4Srikanth Prabhu5Krishnaraj Chadaga6Senior Data Scientist, Torcai Digital Media Pvt. Ltd, Mumbai, IndiaSchool of Information Technology, Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaSchool of Computer Science, University of Petroleum and Energy Studies, Dehradun, IndiaDepartment of Electrical and Electronics Engineering, Trinity College of Engineering and Technology, Karimnagar, IndiaDepartment of Information Technology, Manipal University Jaipur, Jaipur, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaAbstractAlzheimer’s disease is a gradual neurodegenerative condition affecting the brain, causing a decline in cognitive function by progressively damaging nerve cells over time. While a cure for Alzheimer’s remains elusive, the detection of Alzheimer’s disease (AD) through brain biomarkers is crucial to impede its advancement. High-resolution structural MRI scans, particularly T1-weighted images, are commonly used in Alzheimer’s detection. These images provide detailed information about the brain’s structure, allowing researchers and clinicians to identify abnormalities. Our study employs a deep learning methodology using T1-weighted MRI images for a binary classification task—distinguishing between AD and normal/healthy control (NC). The volumetric convolutional neural network model is deployed on pre-processed images and validated on MIRIAD datasets, achieving an impressive accuracy of 97%, surpassing other network models. Addressing the challenge of limited datasets for deep learning models, we incorporated various augmentation techniques such as rotation and rescaling, resulting in outstanding model accuracy and effective discerning between Alzheimer’s disease and normal controls.https://www.tandfonline.com/doi/10.1080/23311916.2024.2314872Alzheimer’s diseaseneuroimagingdeep learningmagnetic resonance imagingconvolutional neural networkJin Zhongmin |
| spellingShingle | Nitika Goenka Akhilesh Kumar Sharma Shamik Tiwari Nagendra Singh Vyom Yadav Srikanth Prabhu Krishnaraj Chadaga A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images Cogent Engineering Alzheimer’s disease neuroimaging deep learning magnetic resonance imaging convolutional neural network Jin Zhongmin |
| title | A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images |
| title_full | A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images |
| title_fullStr | A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images |
| title_full_unstemmed | A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images |
| title_short | A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images |
| title_sort | regularized volumetric convnet based alzheimer detection using t1 weighted mri images |
| topic | Alzheimer’s disease neuroimaging deep learning magnetic resonance imaging convolutional neural network Jin Zhongmin |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2024.2314872 |
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