Abstract
Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importance for patient care and efficient treatment. Neuroimaging techniques aim to assist the physician in the diagnosis of brain disorders by using images. Positron emission tomography (PET) is a kind of neuroimaging technique employed to create 3D images of the brain. Due to many PET images, researchers attempted to develop computer-aided diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods used image processing techniques for preprocessing and attributes extraction and then developed a model or classifier to classify the brain images. As a result, the retrieved features had a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on a convolutional neural network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients. The proposed approach is evaluated using the 18FDG-PET images of 855 patients, including 635 normal control and 220 Alzheimer's disease patients from the ADNI database. The result showed that the proposed CAD system yields an accuracy of 96%, a sensitivity of 96%, and a specificity of 94%, leading to splendid performance when related to the methods already in use that are specified in the literature.
Highlights
Alzheimer’s disease (AD) is a type of brain disease which regularly affects people over 65 years old
To estimate the efficacy of the proposed computer-aided diagnosis (CAD) system, this paper evaluates some widely used performance indicators, such as confusion matrix, region of curve (ROC), classification accuracy, sensitivity, and specificity
A CAD device for discriminating Alzheimer’s Disease (AD) from normal control (NC) patients based on features from 18FDG-Positron emission tomography (PET) images was proposed and investigated properly
Summary
Alzheimer’s disease (AD) is a type of brain disease which regularly affects people over 65 years old. It is a progressive and neurodegenerative disorder, meaning that, it becomes worse with time. According to an Alzheimer’s Association report, around 55 million people are living with AD worldwide and it is envisioned that the number of AD patients will reach 152 million by 2,050. The number of people with AD is progressively increasing worldwide. The prime objective of this paper is to develop a robust classification system for AD diagnosis using a convolutional neural network (CNN). In this approach, the 3D image classification problem is completely
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