An ensemble of CNN architectures for early detection of alzheimer’s disease using brain MRI

Abstract

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.