🌟 Excited to unveil my latest endeavor, "Early Detection of Alzheimer's Disease Using Ensemble Learning Technique". The objective of the Alzheimer's Disease Detection Using Ensemble Approach is to develop a reliable and accurate method for diagnosing Alzheimer's disease at an early stage using medical imaging data, particularly 3D MRI scans.The goal is to help clinicians and medical professionals make an accurate diagnosis of Alzheimer's disease in its early stages, which can greatly improve patient outcomes by enabling earlier intervention and treatment!.
Alzheimer's disease is a neurodegenerative disease that usually starts slowly and progressively worsens, and is the cause of 60–70% of cases of dementia. The most common early symptom is difficulty in remembering recent events. As the disease advances, symptoms can include problems with language, disorientation (including easily getting lost), mood swings, loss of motivation, self-neglect, and behavioral issues. As a person's condition declines, they often withdraw from family and society. Gradually, bodily functions are lost, ultimately leading to death. Although the speed of progression can vary, the typical life expectancy following diagnosis is three to nine years.
- The current systems for early detection of Alzheimer's disease using 3D deep ensemble approach involve the combination of various deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders to analyze brain scans.
- The drawback of this system is that it is computationally intensive which require significant resources to train, fine-tune and use of inference it will be difficult to use in resource-constrained environment such as mobile devices or low-power systems.
The dataset is obtained from the ADNI (Alzheimers Disease Neuroimaging Initiative)
In this project, we present an early diagnosis system for Alzheimer’s disease that recognizes AD, MCI, and CN using MRI images. To address the problem of data leakage, we demonstrated a system that uses only the first visit of each patient and ignores the others. Extensive comparative investigations were performed to study the effects of using various data types from the ADNI and the size of the dataset.