A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images

Abstract

Alzheimer’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.

Keywords:

• Alzheimer’s disease
• neuroimaging
• deep learning
• magnetic resonance imaging
• convolutional neural network

Reviewing Editor:

• Jin Zhongmin
• Xian Jiao Tong University (China) and Leeds University (UK)
• Professor
• CHINA

SUBJECTS:

• Technology
• Computer Science
• Artificial Intelligence
• Computation
• Computer Engineering

Data availability statement

The MIRIAD database provided the data that was used to prepare this article. The MIRIAD investigators were not involved in the report’s analysis or composition. The UK Alzheimer’s Society’s assistance allows for the availability of the MIRIAD dataset (Grant RF116). GlaxoSmithKline provided an unrestricted educational grant to support the first data gathering (Grant 6GKC). Data will be made available by the corresponding author upon prior request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Akhilesh Kumar Sharma

Akhilesh Kumar Sharma is the Head of Department, Data Science and Engineering, Manipal University Jaipur, Jaipur, India. He has published over 70 articles in journals and conferences and written books and book chapters. He has six patents and four copyrights to his credit.