% Chapter 1 of the Thesis Template File
%\tracingpatches
\addtocontents{toc}{\def\protect\@chapapp{CHAPTER\ }}
\chapter{INTRODUCTION}
\justify
\par Alzheimer's disease is a chronic neuro degenerative disease that usually starts slowly and worsens over time. It is the main cause of 60-70$\%$ of the cases of dementia.There are different stages of the disease like mild stage, moderate stage and crucial stage. Advanced medical imaging techniques like MRI,CT,PET etc shows significant role in the diagnosis of the disease.We used brain MRI images for the classification purpose.From the ADNI(Alzheimer Disease Neuro Imaging)database we selected 2700 images consisting of normal control, Mild Cognitive Impairment (MCI) and Alzheimer's Disease(AD). …show more content…
Early symptom is difficulty in remembering recent events.As the disease progresses,symptoms can include problems with language , disorientation, mood swings and behavioral issues. Risk factors include genetics,head injuries ,depression and hypertension. The disease was described by German phychiatrist and pathologist Alois Alzheimer. The abnormalities most evident in brains of diseased people are loss of neuronal connections and cell death. Amyloid plaques,neurofibrillary tangles,synaptic loss and cell death are the striking features of Alzheimer's brain. Many other changes occur in the brain cell during disease progression. For example,glial cells swell up and divide to produce more number of cells. More than 90$\%$ of the disease occur in people above age 60. Some people with memory problem may have mild cognitive impairment(MCI), a condition that may lead to Alzheimer's disease. Major tools of the disease diagnosis includes analysing medical history of the patient, a physical exam, and tests which measure memory , language skills and other abilities related to brain functioning. Neuro psychological tests such as MMSE(Mini Mental State Examination) are used for diagnosis as the screening test. Low MMSE score needs further evaluation such as brain imaging …show more content…
As the initial step, MRI is pre-processed. Feature extraction and feature reduction processes are the main steps before classification. The main objectives are as follows.
\begin{itemize}
\item Extraction of textural features from the pre-processed MRI
\item Reduction of features into required dimension using Principal Component Analysis
\item Classification of MRI into NC,MCI and AD based on the extracted features
\item Compare the classification accuracy of three classifiers: SVM, ANN and k-NN
\item Compare the classification accuracy with original and reduced features
\end{itemize}
\subsection{Scope}
Current diagnosis of Alzheimer's disease is made by clinical,mental and neuro-physiological tests. Routine structural neuroimaging evaluation is based on nonspecific features such as atrophy, which is a late feature in the progression of the disease. Therefore, developing new approaches for early and specific recognition of Alzheimer's disease is of crucial importance. The disease detection from the brain MRI can be carried out by extracting some relevant features of the diseased image. Feature values show variation for different stages of the disease.Machine learning is employed for the classification of given brain MRI into normal,MCI and AD
%\tracingpatches
\addtocontents{toc}{\def\protect\@chapapp{CHAPTER\ }}
\chapter{INTRODUCTION}
\justify
\par Alzheimer's disease is a chronic neuro degenerative disease that usually starts slowly and worsens over time. It is the main cause of 60-70$\%$ of the cases of dementia.There are different stages of the disease like mild stage, moderate stage and crucial stage. Advanced medical imaging techniques like MRI,CT,PET etc shows significant role in the diagnosis of the disease.We used brain MRI images for the classification purpose.From the ADNI(Alzheimer Disease Neuro Imaging)database we selected 2700 images consisting of normal control, Mild Cognitive Impairment (MCI) and Alzheimer's Disease(AD). …show more content…
Early symptom is difficulty in remembering recent events.As the disease progresses,symptoms can include problems with language , disorientation, mood swings and behavioral issues. Risk factors include genetics,head injuries ,depression and hypertension. The disease was described by German phychiatrist and pathologist Alois Alzheimer. The abnormalities most evident in brains of diseased people are loss of neuronal connections and cell death. Amyloid plaques,neurofibrillary tangles,synaptic loss and cell death are the striking features of Alzheimer's brain. Many other changes occur in the brain cell during disease progression. For example,glial cells swell up and divide to produce more number of cells. More than 90$\%$ of the disease occur in people above age 60. Some people with memory problem may have mild cognitive impairment(MCI), a condition that may lead to Alzheimer's disease. Major tools of the disease diagnosis includes analysing medical history of the patient, a physical exam, and tests which measure memory , language skills and other abilities related to brain functioning. Neuro psychological tests such as MMSE(Mini Mental State Examination) are used for diagnosis as the screening test. Low MMSE score needs further evaluation such as brain imaging …show more content…
As the initial step, MRI is pre-processed. Feature extraction and feature reduction processes are the main steps before classification. The main objectives are as follows.
\begin{itemize}
\item Extraction of textural features from the pre-processed MRI
\item Reduction of features into required dimension using Principal Component Analysis
\item Classification of MRI into NC,MCI and AD based on the extracted features
\item Compare the classification accuracy of three classifiers: SVM, ANN and k-NN
\item Compare the classification accuracy with original and reduced features
\end{itemize}
\subsection{Scope}
Current diagnosis of Alzheimer's disease is made by clinical,mental and neuro-physiological tests. Routine structural neuroimaging evaluation is based on nonspecific features such as atrophy, which is a late feature in the progression of the disease. Therefore, developing new approaches for early and specific recognition of Alzheimer's disease is of crucial importance. The disease detection from the brain MRI can be carried out by extracting some relevant features of the diseased image. Feature values show variation for different stages of the disease.Machine learning is employed for the classification of given brain MRI into normal,MCI and AD