Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning

Abstract

The neurodegenerative Alzheimer's Disease is the most widely recognized cause of 'Dementia' and was allegedly the 7th highest cause of death globally. Yet, there is still no conclusive test for distinguishing Alzheimer's disease. Our proposed model eliminates these challenges in a significant manner. The technique is fit for investigating and analyzing different classes in a single setting and requires significantly less previous apprehension. Several handcrafted or predefined machine learning and deep learning models have been imple-mented in this field of study. Our proposed multi-classification model is primarily implemented based on the Open Access Series of Imaging Studies (OASIS) data and suggests an 18-layer architecture. We have implemented a unique preprocessing approach using all three anatomical planes of the MRI scans in a single sequential model, which was also evaluated afterwards. The research also explores a comparative study among multiple and binary classes in terms of performance and efficiency. Pre-defined models such as Inception V3and VGG19 have also been brought to comparison to measure the model's reliability. Our multiclass setting shows an accuracy of over 80%, which is higher than most of the existing multi-classification models in this dataset. Moreover, the in-depth comparative study using binary classification shows a significant accuracy of over 92%, which ensures the all-Around efficacy of the model.