8Alzheimer’s Detection and Classification Using Fine-Tuned Convolutional Neural Network

Anooja Ali1*, Sarvamangala D. R.2, Meenakshi Sundaram A.1 and Rashmi C.2

1School of Computer Science and Engineering, REVA University, Bengaluru, India

2School of Computing and Information Technology, REVA University, Bengaluru, India


Alzheimer’s disease (AD) is a neurocognitive disorder and it evolves into the death of nerve cells. After the age of 60, the risk of developing the illness doubles every five years, with estimates that by 2050, the number will have risen to 135 million. Brain structural image with magnetic resonance imaging (MRI) has been extensively utilized to recognize AD as it can detect morphometric variations and cerebral congenital malformations. Convolutional neural networks (CNNs) are extensively used for image receptions and analysis because of their capacity to handle enormous amounts of unstructured data and retrieve significant characteristics automatically. A new approach involving pretrained CNN model, VGG16 with fine tuning has been proposed for automatic detection and classification of brain MRI images for AD. The results show that the performance of the proposed modeling terms of accuracy, f1-score, recall and precision is above 90%.

Keywords: ADNet, Alzheimer’s disease, dementia, transfer learning

8.1 Introduction

The human brain is a sophisticated organ with millions of neurons that communicate data via electrical and chemical impulses. Alzheimer’s ...

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