학술논문
An ensemble of CNN architectures for early detection of alzheimer's disease using brain MRI
Document Type
Clinical report
Source
Mehran University Research Journal of Engineering and Technology. October 2023, Vol. 42 Issue 4, p140, 8 p.
Subject
Language
English
ISSN
0254-7821
Abstract
1. Introduction Alzheimer's disease (AD), the most prevalent type of dementia in the senior population, is a progressive, degenerative brain condition that gradually impairs memory and cognitive function. Almost 50 [...]
Early detection of Alzheimer's disease (AD) has proven to be helpful and effective in preventing the disease. If the risks and symptoms of AD are detected earlier, then it seems rather promising that the death ratio of AD might decrease as it can help a lot of patients get treated before it's too late. Our study demonstrates promising results, achieving a remarkable accuracy of 96.52% through the utilization of the EfficientNetB2 and EfficientNetB3 models. By leveraging transfer learning, we leverage pre-trained models' knowledge to optimize the learning process, while ensemble learning further improves performance by aggregating predictions from multiple models. The integration of these methodologies provides an effective and efficient means of detecting Alzheimer's Disease at an early stage, thereby offering potential benefits to patients, caregivers, and healthcare providers alike. These findings pave the way for improved diagnostic tools and contribute to the advancement of AD research and patient care. KEYWORDS Alzheimer's Disease Detection Ensembling Deep Learning Transfer Learning
Early detection of Alzheimer's disease (AD) has proven to be helpful and effective in preventing the disease. If the risks and symptoms of AD are detected earlier, then it seems rather promising that the death ratio of AD might decrease as it can help a lot of patients get treated before it's too late. Our study demonstrates promising results, achieving a remarkable accuracy of 96.52% through the utilization of the EfficientNetB2 and EfficientNetB3 models. By leveraging transfer learning, we leverage pre-trained models' knowledge to optimize the learning process, while ensemble learning further improves performance by aggregating predictions from multiple models. The integration of these methodologies provides an effective and efficient means of detecting Alzheimer's Disease at an early stage, thereby offering potential benefits to patients, caregivers, and healthcare providers alike. These findings pave the way for improved diagnostic tools and contribute to the advancement of AD research and patient care. KEYWORDS Alzheimer's Disease Detection Ensembling Deep Learning Transfer Learning