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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50

Author(s): V P Nithya*, N Mohanasundaram and R. Santhosh

Volume 20, 2024

Published on: 16 October, 2023

Article ID: e250823220361 Pages: 20

DOI: 10.2174/1573405620666230825113344

open_access

Open Access Journals Promotions 2
Abstract

Objective: The objective of this study is to develop a more effective early detection system for Alzheimer's disease (AD) using a Deep Residual Network (ResNet) model by addressing the issue of convolutional layers in conventional Convolutional Neural Networks (CNN) and applying image preprocessing techniques.

Methods: The proposed method involves using Contrast Limited Adaptive Histogram Equalizer (CLAHE) and Boosted Anisotropic Diffusion Filters (BADF) for equalization and noise removal and K-means clustering for segmentation. A ResNet-50 model with shortcut links between three residual layers is proposed to extract features more efficiently. ResNet-50 is preferred over other ResNet types due to its intermediate depth, striking a balance between computational efficiency and improved performance, making it a widely adopted and effective architecture for various computer vision tasks. While other ResNet variations may offer higher depths, they are more prone to overfitting and computational complexity, which can hinder their practical application. The proposed method is evaluated on a dataset of MRI scans of AD patients.

Results: The proposed method achieved high accuracy and minimum losses of 95% and 0.12, respectively. While some models showed better accuracy, they were prone to overfitting. In contrast, the suggested framework, based on the ResNet-50 model, demonstrated superior performance in terms of various performance metrics, providing a robust and reliable approach to Alzheimer's disease categorization.

Conclusion: The proposed ResNet-50 model with shortcut links between three residual layers, combined with image preprocessing techniques, provides an effective early detection system for AD. The study demonstrates the potential of deep learning and image processing techniques in developing accurate and efficient diagnostic tools for AD. The proposed method improves the existing approaches to AD classification and provides a promising framework for future research in this area.

Keywords: Alzheimer's disease, ResNet, CLAHE, Boosted anisotropic diffusion filter, K-means clustering, Convolutional Neural Networks (CNN).

[1]
Lukiw WJ, Vergallo A, Lista S, Hampel H, Zhao Y. Biomarkers for Alzheimer’s Disease (AD) and the application of precision medicine. J Pers Med 2020; 10(3): 138.
[http://dx.doi.org/10.3390/jpm10030138] [PMID: 32967128]
[2]
Sabbagh MN, Blennow K. Peripheral biomarkers for Alzheimer’s Disease: Update and progress. Neurol Ther 2019; 8(S2) (2): 33-6.
[http://dx.doi.org/10.1007/s40120-019-00171-6] [PMID: 31833022]
[3]
Husain M. Blood tests to screen for Alzheimer’s disease. Brain 2021; 144(2): 355-6.
[http://dx.doi.org/10.1093/brain/awaa462] [PMID: 33693691]
[4]
Lane CA, Hardy J, Schott JM. Alzheimer’s disease. Eur J Neurol 2018; 25(1): 59-70.
[http://dx.doi.org/10.1111/ene.13439] [PMID: 28872215]
[5]
Matej R, Tesar A, Rusina R. Alzheimer’s disease and other neurodegenerative dementias in comorbidity: A clinical and neuropathological overview. Clin Biochem 2019; 73: 26-31.
[http://dx.doi.org/10.1016/j.clinbiochem.2019.08.005] [PMID: 31400306]
[6]
Delmastro F, Di Martino F, Dolciotti C. Cognitive training, and stress detection in MCI frail older people through wearable sensors and machine learning. IEEE Access 2020; 8: 65573-90.
[7]
Liu SQ, Liu SD, Cai WD. Early diagnosis of Alzheimer’s disease with deep learning. In Proceedings of the 2014 IEEE 11t; International Symposium on Biomedical Imaging (ISBI). April–2 May, Beijing, China, pp, 1015–1018.
[http://dx.doi.org/10.1109/ISBI.2014.6868045]
[8]
Brugnolo A, Girtler N, Doglione E, et al. Brain resources: How semantic cueing works in mild cognitive impairment due to Alzheimer’s Disease (MCI-AD). Diagnostics 2021; 11(1): 108.
[http://dx.doi.org/10.3390/diagnostics11010108] [PMID: 33445437]
[9]
Guo M, Li Y, Zheng W, et al. A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs. J Neurol 2020; 267(10): 2983-97.
[http://dx.doi.org/10.1007/s00415-020-09890-5] [PMID: 32500373]
[10]
Counts SE, Ikonomovic MD, Mercado N, Vega IE, Mufson EJ. Biomarkers for the early detection and progression of Alzheimer’s Disease. Neurotherapeutics 2017; 14(1): 35-53.
[http://dx.doi.org/10.1007/s13311-016-0481-z] [PMID: 27738903]
[11]
Hampel H, Frank R, Broich K, et al. Biomarkers for Alzheimer’s disease: Academic, industry and regulatory perspectives. Nat Rev Drug Discov 2010; 9(7): 560-74.
[http://dx.doi.org/10.1038/nrd3115] [PMID: 20592748]
[12]
Zhao A, Li Y, Yan Y, et al. Increased prediction value of biomarker combinations for the conversion of mild cognitive impairment to Alzheimer’s dementia. Transl Neurodegener 2020; 9(1): 30.
[http://dx.doi.org/10.1186/s40035-020-00210-5] [PMID: 32741361]
[13]
Lonie JA, Tierney KM, Ebmeier KP. Screening for mild cognitive impairment: A systematic review. Int J Geriatr Psychiatry 2009; 24(9): 902-15.
[http://dx.doi.org/10.1002/gps.2208] [PMID: 19226524]
[14]
Choe YM, Lee BC, Choi IG, Suh GH, Lee DY, Kim JW. MMSE subscale scores as useful predictors of AD conversion in mild cognitive impairment. Neuropsychiatr Dis Treat 2020; 16: 1767-75.
[http://dx.doi.org/10.2147/NDT.S263702] [PMID: 32801712]
[15]
Milian M, Leiherr AM, Straten G, Müller S, Leyhe T, Eschweiler GW. The mini-cog versus the mini-mental state examination and the clock drawing test in daily clinical practice: Screening value in a german memory clinic. Int Psychogeriatr 2012; 24(5): 766-74.
[http://dx.doi.org/10.1017/S1041610211002286] [PMID: 22172089]
[16]
Lopez-Font I, Cuchillo-Ibañez I, Sogorb-Esteve A, García-Ayllón MS, Sáez-Valero J. Transmembrane amyloid-related proteins in CSF as potential biomarkers for Alzheimer’s Disease. Front Neurol 2015; 6: 125.
[http://dx.doi.org/10.3389/fneur.2015.00125] [PMID: 26082753]
[17]
Delaby C, Muñoz L, Torres S, et al. Impact of CSF storage volume on the analysis of Alzheimer’s disease biomarkers on an automated platform. Clin Chim Acta 2019; 490: 98-101.
[http://dx.doi.org/10.1016/j.cca.2018.12.021] [PMID: 30579960]
[18]
Mohanty R, Mårtensson G, Poulakis K, et al. Towards harmonizing subtyping methods for PET and MRI studies of Alzheimer’s disease. Alzheimers Dement 2020; 16(S4): e042807.
[http://dx.doi.org/10.1002/alz.042807]
[19]
Ottoy J, Niemantsverdriet E, Verhaeghe J, et al. Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging. Neuroimage Clin 2019; 22: 101771.
[http://dx.doi.org/10.1016/j.nicl.2019.101771] [PMID: 30927601]
[20]
Pagani M, Nobili F, Morbelli S, et al. Early identification of MCI converting to AD: a FDG PET study. Eur J Nucl Med Mol Imaging 2017; 44(12): 2042-52.
[http://dx.doi.org/10.1007/s00259-017-3761-x] [PMID: 28664464]
[21]
Choi BK, Madusanka N, Choi HK, et al. Convolutional neural network-based mr image analysis for Alzheimer’s disease classification. Curr Med Imaging Rev 2020; 16(1): 27-35.
[http://dx.doi.org/10.2174/1573405615666191021123854] [PMID: 31989891]
[22]
Zhang X, Zong B, Zhao W, Li L. Effects of mind-body exercise on brain structure and function a systematic review on MRI studies. Brain Sci 2021; 11(2): 205.
[http://dx.doi.org/10.3390/brainsci11020205] [PMID: 33562412]
[23]
Ren F, Yang C, Qiu Q, et al. Exploiting discriminative regions of brain slices based on 2D CNNs for Alzheimer’s Disease classification IEEE Access 2019; 7: 181423-33.
[24]
Sumanth S, Suresh A. Survey on the identification of Alzheimer’s disease using magnetic resonance imaging (MRI) images. Int. J. Innov. Technol. Explore Eng 2019; 8: 3564-70.
[25]
D’Angelo G, Palmieri F. GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems. Inf Sci 2021; 547: 136-62.
[http://dx.doi.org/10.1016/j.ins.2020.08.040]
[26]
Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A deep siamese convolution neural network for multi-class classification of alzheimer disease. Brain Sci 2020; 10(2): 84.
[http://dx.doi.org/10.3390/brainsci10020084] [PMID: 32033462]
[27]
Al-Shoukry S, Rassem TH, Makbol NM. Alzheimer’s Diseases detection by using deep learning algorithms. A Mini-Review; IEEE Access 2020; 8: 77131-41.
[28]
Amini M, Pedram MM, Moradi A, Ouchani M. Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN). Comput Math Methods Med 2021; 2021: 1-15.
[http://dx.doi.org/10.1155/2021/5514839] [PMID: 34007305]
[29]
Ding Y, Jae Ho S, Michael GK, et al. A deep learning model to predict a diagnosis of Alzheimer's disease by using 18F-FDG PET of the brain. Radiology 2019; 2: 456-64.
[30]
Ebrahim D, Amr MT AE, Hossam EM, Hesham A. Alzheimer disease early detection using convolutional neural networks. In 2020 15th International Conference on Computer Engineering and Systems (ICCES) 2020; 1-6.
[31]
Suh C H, Shim W H, Kim S J, et al. Development and validation of a deep learning-based automatic brain segmentation and classification algorithm for Alzheimer's disease using 3D T1-weighted volumetric image. Am J Neuroradiol 2020; 12: 2227-34.
[32]
Salehi AW, Preety B, Brij BS, Gaurav G, Ankita U. A CNN model: Earlier diagnosis and classification of Alzheimer's disease using MRI. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) 2020; 156-61.
[33]
Kundaram SS, Ketki CP. Deep learning-based Alzheimer disease detection; In proceedings of the fourth international conference on microelectronics, computing and communication systems. 2021; 587-97.
[34]
Al-Khuzaie , Fanar EK, Oguz B, Adil DD. Diagnosis of Alzheimer's disease using 2D MRI slices by a convolutional neural network. Applied Bionics and Biomechanics 2021; 2021
[35]
Nawaz H, Muazzam M, Sitara A, Farhan A, Irfan M, Seungmin Rho. A deep feature-based real-time system for Alzheimer's disease stage detection; Multimedia Tools and Applications. 2021; 28: 35789-807.
[36]
Carvalho ED, Carvalho ED, de Carvalho Filho AO, de Sousa AD, de Andrade Lira Rabulo R. COVID-19 diagnosis in CT images using CNN to extract features and multiple classifiers. IEEE Xplore. 26-28 October 2020, Cincinnati, OH, USA, pp. 425–431.
[37]
Kaur A, Singh C. Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Appl Soft Comput 2017; 51: 180-91.
[http://dx.doi.org/10.1016/j.asoc.2016.11.046]
[38]
Sonker D. Comparison of histogram equalization techniques for image enhancement of grayscale images in natural and unnatural light. Int j eng res 2013; 8(9): 57-61.
[39]
Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE. Contrast-limited adaptive histogram equalization: speed and effectiveness Proceedings of the first conference on visualization in biomedical computing. 22-25 May 1990, Atlanta, GA, USA, pp. 337-345.
[http://dx.doi.org/10.1109/VBC.1990.109340]
[40]
Pisano ED, Zong S, Hemminger BM, et al. Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms. J Digit Imaging 1998; 11(4): 193-200.
[http://dx.doi.org/10.1007/BF03178082] [PMID: 9848052]
[41]
Reza AM. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Signal Process 2004; 38(1): 35-44.
[http://dx.doi.org/10.1023/B:VLSI.0000028532.53893.82]
[42]
Shehroz S, Khan AA. Cluster centre initialization algorithm for k-means cluster. Pattern Recognit Lett 2004; 1293-302.
[43]
Sorin S. An overview on clustering methods OSR J Eng 2004; 2(4): 719-25.
[44]
Abdul AS, Mohd YM, Zeehaida M, Abdul Aimi Salihai. Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering. In WSEAS Transaction on Biology and Biomedicine 2013; 10(1): 41-55.
[45]
Beheshti I, Demirel H, Matsuda H, Initiative ADN. Classification of Alzheimer’s disease and prediction of mild cognitive impairment-to-Alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 2017; 83: 109-19.
[http://dx.doi.org/10.1016/j.compbiomed.2017.02.011] [PMID: 28260614]
[46]
Suk HI, Lee SW, Shen D. Alzheimer’s Disease Neuroimaging Initiative Deep ensemble learning of sparse regression models for brain disease diagnosis. Med Image Anal 2017; 37: 101-13.
[http://dx.doi.org/10.1016/j.media.2017.01.008] [PMID: 28167394]
[47]
Shi B, Chen Y, Zhang P, Smith CD, Liu J. Nonlinear feature transformation and deep fusion for Alzheimer’s Disease staging analysis. Pattern Recognit 2017; 63: 487-98.
[http://dx.doi.org/10.1016/j.patcog.2016.09.032]
[48]
Liu M, Zhang D, Shen D. Alzheimer’s Disease Neuroimaging Initiative Hierarchical fusion of features and classifier decisions for Alzheimer’s disease diagnosis. Hum Brain Mapp 2014; 35(4): 1305-19.
[http://dx.doi.org/10.1002/hbm.22254] [PMID: 23417832]
[49]
Aderghal K, Benois-Pineau J, Afdel K. Classification of sMRI for Alzheimer's disease Diagnosis with CNN: Single Siamese Networks with 2D+? Approach and Fusion on ADNI Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval 2017; 494-8.
[http://dx.doi.org/10.1145/3078971.3079010]
[50]
Islam J, Yanqing Z. Alzheimer’s disease neuroimaging initiative. Deep convolutional neural networks for automated diagnosis of Alzheimer’s disease and mild cognitive impairment using 3D brain MRI. In International Conference on Brain Informatics 2018; 359-69.
[51]
Altunbey Özbay F, Özbay E. An NCA-based Hybrid CNN Model for Classification of Alzheimer’s Disease on Grad-CAM-enhanced Brain MRI Images. Turkish JAF SciTech 2023; 18(1): 139-55.
[http://dx.doi.org/10.55525/tjst.1212513]
[52]
Özbay F A, Özbay E. Brain tumor detection with mRMR-based multimodal fusion of deep learning from MR images using Grad-CAM. Iran J Comput Sci 2023; 1-15.
[53]
Eroglu Y, Yildirim M, Cinar A. MRMR ‐based hybrid convolutional neural network model for classification of Alzheimer’s disease on brain magnetic resonance images. Int J Imaging Syst Technol 2022; 32(2): 517-27.
[http://dx.doi.org/10.1002/ima.22632]
[54]
Asif S, Yildirim M, Cinar A. An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning. Multimed Tools Appl 2023; 1-28.
[55]
Asif S, Yi W, Ain QU, Hou J, Yi T, Si J. Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from MR images. IEEE Access 2022; 10: 34716-30.
[http://dx.doi.org/10.1109/ACCESS.2022.3153306]
[56]
Wongvorachan T, Surina H, Okan B. Comparison of undersampling, oversampling, and SMOTE methods for dealing with imbalanced classification in educational data mining. 2023; 14(1): 54.

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