Abstract
Globally, Neurological disorders are a major health concern affecting a population of billions worldwide. There’s a need for accurate and timely diagnosis of brain disorders to improve patient outcomes and revolutionize the field of medicine with the help of technology. For this, the integration of deep learning models with MRI (structural and functional) images presents a promising approach for the detection of brain disorders like Alzheimer’s disease. Our Research aims to develop and evaluate deep learning models for detecting Alzheimer’s disease using the Oasis dataset, a popularly used data set of neuroimaging and processed imaging data, for brain images of Alzheimer patients. There were 2 types of images i.e. the Raw and FSL-SEG (preprocessed) gifs. The models were developed using multiple Convolution layers and a Non-linear activation function (Sigmoid) for binary classification. Early stopping on loss helped prevent overfitting, and a batch size of 75 was used for faster convergence. We generated an accuracy of 90% on the FSL-SEG MRI images whereas the RAW images resulted in an accuracy of 83%. With a value of 0.79 in Area Under the Curve, The CDR (Clinical Dementia Rating) as well as MMSE (Mini Mental State Examination) were main factors which interlinked the images with occurence of Alzheimer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Garner, R., Ghariq, E., Vasavada, M.M., Singh, K., Carney, P.R.: Machine learning model to predict seizure susceptibility from resting-state fMRI connectivity. Front. Neurol. 12, 715929 (2021)
Shoeibi, A., Khosravi, A., Wang, X.: An overview of deep learning tech niques for epileptic seizures detection and prediction based on neuroimaging modalities: methods, challenges, and future works. Front. Neurosci. 15, 650669 (2021)
Kruthikaa, K.R., Rajeswari, B., Maheshappa, H.D.: CBIR system using Capsule Networks and 3D CNN for Alzheimer’s disease diagnosis. Alzheimer’s Dis ease Neuroimaging Initiative1 (2021)
Basaia, S., et al.: Automated classification of Alzheimer’s disease and mild cognitive im pairment using a single MRI and deep neural networks. Alzheimer’s Disease Neu roimaging Initiative1 (2021)
Aaraji, Z., Abbas, H.H.: Automatic Classification of Alzheimer’s Disease using brain MRI data and deep Convolutional Neural Networks. IEEE Access 9, 45241–45252 (2021)
Basheer, S., Bhatia, S., Sakri, S.B.: Computational Modeling of Demen tia Prediction Using Deep Neural Network: Analysis on OASIS Dataset (2021)
Christensen, D.V., et al.: 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Comput. Eng. 2(2), 022501 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Saigiridhari, A., Mishra, A., Tupe, A., Yedurkar, D., Galphade, M. (2024). Deep CNN Based Alzheimer Analysis in MRI Using Clinical Dementia Rating. In: Muthalagu, R., P S, T., Pawar, P.M., R, E., Prasad, N.R., Fiorentino, M. (eds) Computational Intelligence and Network Systems. CINS 2023. Communications in Computer and Information Science, vol 1978. Springer, Cham. https://doi.org/10.1007/978-3-031-48984-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-48984-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48983-9
Online ISBN: 978-3-031-48984-6
eBook Packages: Computer ScienceComputer Science (R0)