Classification of OCT images for the early diagnosis of Alzheimer's disease

Abstract

Alzheimer's disease (AD) is a common form of senile dementia. Although our understanding of the key steps underlying neurodegeneration in Alzheimer's disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. The solution for this is the employment of advanced Biomedical Engineering Technology for making diagnosis. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer's kind pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. The parameters of the human body related to AD can be profiled using computers that can be utilized for the early diagnosis of Alzheimer's disease. There are several imaging techniques used in clinical practice for the diagnosis of Alzheimer's type pathology. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography Scanning (SPECT) and Optical Coherence Tomography (OCT). The previous and recent studies made on Alzheimer's disease clearly investigated that there are some parameter changes on the retina of the eye of AD patients. In this research we have classify the OCT retinal images using neural networks for the early diagnosis of AD.