Abstract
Dementia is a brain disorder that causes loss of memory leading to disruption in the normal course of life of an individual. It is emerging as a global health problem in adults with age 65 years or above. Early diagnosis of dementia has gone forth as a key research zone with the aim of early identification for hindering the advancement. Deep learning provides path-breaking applications in medical imaging. This study provides a detailed summary of different implementation approaches of deep learning for detecting the disease. Transfer learning for multi-class classification has also been explored for detecting dementia. The pre-trained convolutional network, AlexNet is used with 3 optimizers, SGDM, ADAM, RMSProp. A Dataset of 60 MRI images is taken from the OASIS dataset. Accuracy of the methods has been compared and the best parameters including classifier, learning rate, and a batch size of the model have been identified. SGDM classifier with a learning rate 10-4 and a mini-batch size of 10 have shown the best performance in a reasonable time.
Highlights
Dementia is a neuropsychological disorder that causes loss of memory leading to disability and dependency on others for survival
Alexnet is trained for three optimizers stochastic gradient descent with momentum (SGDM), adaptive moment estimation (ADAM), and RMSProp, using a learning rate of 0.0001 and a mini-batch size of 10
This paper starts with the basic concepts including the definition of Dementia, its types, and prevalence, followed by the available datasets for the disease and related biomarkers, for example, Magnetic Resonance Imaging (MRI), fMRI, positron emission tomography (PET), etc
Summary
Dementia is a neuropsychological disorder that causes loss of memory leading to disability and dependency on others for survival. Various machine learning techniques implemented for the detection of the disease have been reviewed by various researchers (Bansal et al 2018; Mirzaei et al, 2016; Ahmed et al, 2019). The performance of machine learning approaches is relatively lower with a large amount of data It can be a challenge for the diagnosis of brain disease. Deep learning approaches can overcome the pitfalls of machine learning approaches It can recognize the new features using self-learning of features for the quantitative analysis of MRI. This study would help the researchers to get answers to various research questions listed below pertaining to the implementation of Deep Learning for detecting dementia using MRI images. RQ4: What are the different types of deep learning models used for detecting Dementia?. Multiclass classification is performed for the detection of dementia using MRI images obtained from the OASIS dataset.
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