Abstract
Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.
Highlights
Alzheimer’s Disease (AD) is the most common stages of dementia that requires extensive medical care
The calculation is performed for a total of i) 326 images belonging to ND, ii) 309 images belonging to Very Mild Dementia (VMD), iii) 329 images belonging to MD, and iv) 316 images belonging to Moderate Dementia (MOD)
The DEMentia NETwork (DEMNET) model achieves an accuracy of 84.83%, Area Under Curve (AUC) of 95.62% and Cohen’s kappa score of 0.81 with the same model parameters in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset
Summary
AD is the most common stages of dementia that requires extensive medical care. For initiation of clinical progress and efficient patient treatment, early and precise analysis of AD prediction is necessary [1]. - Severe Dementia: The symptoms may become deteriorated during this stage These patients may lack the capacity to communicate, and full-time treatment may be required for the person. One’s bladder control may be lost, and even small activities are impossible for them to perform actions like keeping their head up in a normal position and sitting in a chair Detection of this disorder is being researched to slow down the abnormal degeneration of the brain, reduce medical care cost reduction, and ensure improved treatment. Extraction and selection of features, reduction of feature dimensionality and classifier algorithm are all phases of the machine learning-based classification process [6] Such techniques need advanced knowledge and several optimization steps, which can be timeconsuming.
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