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Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

Computers, Materials & Continua 2023年第9期76卷 3933-3948页

作者： S M Hasan Mahmud Md Mamun Ali Mohammad Fahim Shahriar Fahad Ahmed Al-Zahrani Kawsar Ahmed Dip Nandi Francis M.Bui Department of Computer Science American International University-Bangladesh(AIUB)Dhaka1229Bangladesh Centre for Advanced Machine Learning and Applications(CAMLAs) DhakaBangladesh Department of Software Engineering Daffodil International UniversityDhakaBangladesh Department of Computer Engineering Umm Al-Qura UniversityMecca24381Saudi Arabia Department of ECE University of SaskatchewanSaskatoonSK S7N5A9Canada Group of Biophotomatiχ Department of ICTMBSTUSantoshTangail1902Bangladesh

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.

关键词： Alzheimer’s disease early detection convolutional neural network data augmentation random oversampling machine learning

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