Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

S Roobini; M S Kavitha; S Karthik

doi:10.4108/eetpht.10.5267

Authors

S Roobini SNS College of Technology
M S Kavitha SNS College of Technology
S Karthik SNS College of Technology

DOI:

https://doi.org/10.4108/eetpht.10.5267

Keywords:

Alzheimer's Disease, AD, Classification, Conventional Neural Network, CNN, Deep Learning, R-CNN, Faster R-CNN, Magnetic Resonance Imaging, MRI

Abstract

INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD).

OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models.

METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN.

RESULTS: On the confusion matrix, R-CNN performed the best.

CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.

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