Functional Brain Image Classification Techniques for Early Alzheimer Disease Diagnosis

DOI: 10.1007/978-3-642-02267-8_17


Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. As the
number of AD patients has increased, its early diagnosis has received more attention for both social and medical reasons.
Single photon emission computed tomography (SPECT), measuring the regional cerebral blood flow, enables the diagnosis even
before anatomic alterations can be observed by other imaging techniques. However, conventional evaluation of SPECT images
often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. This paper
evaluates different pattern classifiers including k-nearest neighbor (kNN), classification trees, support vector machines and feedforward neural networks in combination with template-based normalized
mean square error (NMSE) features of several coronal slices of interest (SOI) for the development of a computer aided diagnosis
(CAD) system for improving the early detection of the AD. The proposed system, yielding a 98.7% AD diagnosis accuracy, reports
clear improvements over existing techniques such as the voxel-as-features (VAF) which yields just a 78% classification accuracy.

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    • "Zhang et al. [9] classified cervix uterus lymphonodus by support vector machine (SVM) and size and shape features. Ramírez et al. [10] proposed to use neural network method in classification of brain images of Alzheimer's disease. However, the research of pancreatic cancer classification is in a fledging period. "
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    ABSTRACT: A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time.
    01/2015; 2015(4):1-12. DOI:10.1155/2015/781023
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    ABSTRACT: Tesis Univ. Granada. Departamento de Teoría de la señal, Telemática y Telecomunicaciones. Leída el 7 de julio de 2010
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    ABSTRACT: The paper introduces two neural network techniques to compare and analyze the detection level of Alzheimer’s disease in a patient. The proposed module uses a Neurological Memory test named Mini Mental Status Examination (MMSE). It is authorized to be used only by neurologist, neuropsychologist and psychiatrist for determining the cognitive level. Doctors use the score of MMSE to evaluate the cognitive level of the patient. According to the method used here, the score below 21 indicates low cognitive level. It uses two Neural Network techniques namely Single Layer Perceptron and Multilayer Feed Forward Perceptron Algorithm. It analyzes each input from the MMSE test and intelligently screens whether the patient is normal or with low Cognitive level thereby reducing the considerable load of doctors or evaluators. The MMSE score also gives an idea to the examiner whether further detailed clinical examination of patients for Alzheimer’s disease is required or not.

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