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Towards Detecting Dementia via Deep Learning

Towards Detecting Dementia via Deep Learning

Deepika Bansal, *Kavita Khanna, Rita Chhikara, Rakesh Kumar Dua, Rajeev Malini
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 17
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859819|DOI: 10.4018/IJHISI.20211001.oa31
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MLA

Bansal, Deepika, et al. "Towards Detecting Dementia via Deep Learning." IJHISI vol.16, no.4 2021: pp.1-17. http://doi.org/10.4018/IJHISI.20211001.oa31

APA

Bansal, D., Khanna, *., Chhikara, R., Dua, R. K., & Malini, R. (2021). Towards Detecting Dementia via Deep Learning. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(4), 1-17. http://doi.org/10.4018/IJHISI.20211001.oa31

Chicago

Bansal, Deepika, et al. "Towards Detecting Dementia via Deep Learning," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.4: 1-17. http://doi.org/10.4018/IJHISI.20211001.oa31

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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.