Research Article
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Ensemble-Based Alzheimer's Disease Classification Using Features Extracted from Hog Descriptor and Pre-trained Models

Year 2024, Volume: 7 Issue: 3, 416 - 426, 31.12.2024

Nedim Muzoglu , Enver Akbacak

https://doi.org/10.35377/saucis...1493368

Abstract

Alzheimer's Disease is the most common type of dementia and is a progressive, neurodegenerative disease. The disease worsens over time, and the patient becomes bedridden, unable to move or understand what is happening around him. The main concern of medicine is to slow down the progression of the disease for which no treatment has yet been developed. Artificial intelligence studies have achieved significant success in detecting many diseases. In this study, an artificial intelligence-based approach that uses MR images of the early stage of Alzheimer's Disease to detect the disease at an early stage is presented. Initially, a new dataset was created through the application of the fuzzy technique, thereby expanding the feature space. Then, an ensemble learning-based hybrid deep learning model was developed to reduce the misclassification rate for all classes. The features derived from the inception module, residual modules, and histogram of oriented gradients descriptor are subjected to classification through bagging and boosting algorithms. The proposed model has surpassed many state-of-the-art studies by achieving a high success rate of 99.60% in detecting Alzheimer's disease in its early stages.

Keywords

Alzheimer disease Hog features Imbalance dataset Bagging and boosting Fuzzy image enhancement

Ethical Statement

The study was prepared within the framework of ethical rules. Since the data set is open access, no additional ethical declaration was received.

References

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  • K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.
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  • C. I. Patel, D. Labana, S. Pandya, K. Modi, H. Ghayvat, and M. Awais, “Histogram of oriented gradient-based fusion of features for human action recognition in action video sequences,” Sensors (Switzerland), vol. 20, no. 24, pp. 1–33, 2020, doi: 10.3390/s20247299.
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  • A. S. Qureshi and T. Roos, “Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets,” Neural Process. Lett., vol. 55, no. 4, pp. 4461–4479, 2023, doi: 10.1007/s11063-022-11049-4.
  • M. N. Hossin, M., Sulaiman, “A Review of Evaluation Metrics in Machine Learning Algorithms,” Med. Image Anal., vol. 80, no. 2, p. 102478, 2022, [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S1361841522001256.
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  • S. Murugan et al., “DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia from MR Images,” IEEE Access, vol. 9, pp. 90319–90329, 2021, doi: 10.1109/ACCESS.2021.3090474.
  • M. M. S. Fareed et al., “ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans,” IEEE Access, vol. 10, no. September, pp. 96930–96951, 2022, doi: 10.1109/ACCESS.2022.3204395.
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  • S. A. Kumar and S. Sasikala, “Enhanced Alzheimer’s Disease Classification Using Multilayer Deep Convolutional Neural Network-Based Experimentations,” Iran. J. Sci. Technol. - Trans. Electr. Eng., vol. 47, no. 4, pp. 1595–1621, 2023, doi: 10.1007/s40998-023-00622-9.

Year 2024, Volume: 7 Issue: 3, 416 - 426, 31.12.2024

Nedim Muzoglu , Enver Akbacak

https://doi.org/10.35377/saucis...1493368

Abstract

References

  • L. C. Arevalo-Flechas, “Latino Alzheimer ’ s Caregiving Neither a Burden nor a Carga,” Univ. ealth Sci. J. Nurs., vol. 1, no. 2, pp. 92–103, 2019.
  • M. ŞENER and H. H. TEKİN, “Sosyal Belediyecilik Bağlamında Yaşlı Bakım ve Alzheimer Gündüz Yaşam Merkezleri,” Geriatr. Bilim. Derg., vol. 3, no. 3, pp. 138–146, 2020, doi: 10.47141/geriatrik.737313.
  • D. S. Knopman et al., “Alzheimer disease,” Nat. Rev. Dis. Prim., vol. 7, no. 1, pp. 1–21, 2021, doi: 10.1038/s41572-021-00269-y.
  • E. Keleş, U. Fzt, S. Özalevli̇, İ. Kâtip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi, and F. ve Rehabilitasyon Bölümü, “Alzheimer Hastalığı ve Tedavi Yaklaşımları Alzheimer’s Disease and Treatment Approaches,” İzmir Katip Çelebi Üniversitesi Sağlık Bilim. Fakültesi Derg., vol. 3, no. 2, pp. 39–42, 2018, [Online]. Available: https://www.alz.org/alzheimers_disease_stages_of_alzheimers.asp.
  • P. S. Aisen, G. A. Jimenez-Maggiora, M. S. Rafii, S. Walter, and R. Raman, “Early-stage Alzheimer disease: getting trial-ready,” Nat. Rev. Neurol., vol. 18, no. 7, pp. 389–399, 2022, doi: 10.1038/s41582-022-00645-6.
  • A. Francis and I. A. Pandian, “Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network,” Multimed. Tools Appl., vol. 80, no. 19, pp. 29585–29600, 2021, doi: 10.1007/s11042-021-11161-y.
  • D. Bansal, K. Khanna, R. Chhikara, R. K. Dua, and R. Malhotra, “Classification of Magnetic Resonance Images using Bag of Features for Detecting Dementia,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 131–137, 2020, doi: 10.1016/j.procs.2020.03.190.
  • M. Shahbaz, S. Ali, A. Guergachi, A. Niazi, and A. Umer, “Classification of Alzheimer’s disease using machine learning techniques,” DATA 2019 - Proc. 8th Int. Conf. Data Sci. Technol. Appl., no. Data, pp. 296–303, 2019, doi: 10.5220/0007949902960303.
  • S. Alam and G. R. Kwon, “Alzheimer disease classification using KPCA, LDA, and multi-kernel learning SVM,” Int. J. Imaging Syst. Technol., vol. 27, no. 2, pp. 133–143, 2017, doi: 10.1002/ima.22217.
  • E. Moradi, A. Pepe, C. Gaser, H. Huttunen, and J. Tohka, “Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects,” Neuroimage, vol. 104, pp. 398–412, 2015, doi: 10.1016/j.neuroimage.2014.10.002.
  • J. A. Dinu and R. Manju, “A novel modelling technique for early recognition and classification of Alzheimer’s disease,” 2021 3rd Int. Conf. Signal Process. Commun. ICPSC 2021, no. May, pp. 21–25, 2021, doi: 10.1109/ICSPC51351.2021.9451803.
  • L. Liu, S. Zhao, H. Chen, and A. Wang, “A new machine learning method for identifying Alzheimer’s disease,” Simul. Model. Pract. Theory, vol. 99, no. November 2019, p. 102023, 2020, doi: 10.1016/j.simpat.2019.102023.
  • X. Zhang, “The AlexNet, LeNet-5 and VGG NET applied to CIFAR-10,” Proc. - 2021 2nd Int. Conf. Big Data Artif. Intell. Softw. Eng. ICBASE 2021, pp. 414–419, 2021, doi: 10.1109/ICBASE53849.2021.00083.
  • C. Szegedy et al., “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015, doi: 10.1109/CVPR.2015.7298594.
  • C. Szegedy et al., “Going Deeper with Convolutions,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.4842.
  • A. Loddo, S. Buttau, and C. Di Ruberto, “Deep learning based pipelines for Alzheimer’s disease diagnosis: A comparative study and a novel deep-ensemble method,” Comput. Biol. Med., vol. 141, no. August 2021, p. 105032, 2022, doi: 10.1016/j.compbiomed.2021.105032.
  • R. Butta, M. S. Shaik, and G. L. N. Murthy, “Ensemble deep learning approach for early diagnosis of Alzheimer’s disease,” Multimed. Tools Appl., no. 0123456789, 2024, doi: 10.1007/s11042-023-18084-w.
  • M. Mujahid, A. Rehman, T. Alam, F. S. Alamri, S. M. Fati, and T. Saba, “An Efficient Ensemble Approach for Alzheimer’s Disease Detection Using an Adaptive Synthetic Technique and Deep Learning,” Diagnostics, vol. 13, no. 15, pp. 1–20, 2023, doi: 10.3390/diagnostics13152489.
  • J. Islam and Y. Zhang, “Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks,” Brain Informatics, vol. 5, no. 2, 2018, doi: 10.1186/s40708-018-0080-3.
  • L. Balagourouchetty, J. K. Pragatheeswaran, B. Pottakkat, and G. Ramkumar, “GoogLeNet-Based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 6, pp. 1686–1694, 2020, doi: 10.1109/JBHI.2019.2942774.
  • M. Guo and Y. Du, “Classification of Thyroid Ultrasound Standard Plane Images using ResNet-18 Networks,” Proc. Int. Conf. Anti-Counterfeiting, Secur. Identification, ASID, vol. 2019-Octob, no. i, pp. 324–328, 2019, doi: 10.1109/ICASID.2019.8925267.
  • O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, Dec. 2015, doi: 10.1007/S11263-015-0816-Y.
  • S. H. Noh, “Analysis of gradient vanishing of RNNs and performance comparison,” Inf., vol. 12, no. 11, 2021, doi: 10.3390/info12110442.
  • C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” 31st AAAI Conf. Artif. Intell. AAAI 2017, pp. 4278–4284, 2017, doi: 10.1609/aaai.v31i1.11231.
  • K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.
  • S. Ruder, “An overview of gradient descent optimization algorithms,” pp. 1–14, 2016, [Online]. Available: http://arxiv.org/abs/1609.04747.
  • C. I. Patel, D. Labana, S. Pandya, K. Modi, H. Ghayvat, and M. Awais, “Histogram of oriented gradient-based fusion of features for human action recognition in action video sequences,” Sensors (Switzerland), vol. 20, no. 24, pp. 1–33, 2020, doi: 10.3390/s20247299.
  • T. Q. Bao, N. T. T. Kiet, T. Q. Dinh, and H. X. Hiep, “Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks,” J. Inf. Telecommun., vol. 4, no. 2, pp. 140–150, 2020, doi: 10.1080/24751839.2019.1666625.
  • M. Hanmandlu and D. Jha, “An optimal fuzzy system for color image enhancement,” IEEE Trans. Image Process., vol. 15, no. 10, pp. 2956–2966, 2006, doi: 10.1109/TIP.2006.877499.
  • S. Banerjee, S. K. Singh, A. Chakraborty, A. Das, and R. Bag, “Melanoma diagnosis using deep learning and fuzzy logic,” Diagnostics, vol. 10, no. 8, 2020, doi: 10.3390/diagnostics10080577.
  • M. Singh, S. Bansal, S. Ahuja, R. K. Dubey, B. K. Panigrahi, and N. Dey, “Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data,” Med. Biol. Eng. Comput., vol. 59, no. 4, pp. 825–839, Apr. 2021, doi: 10.1007/s11517-020-02299-2.
  • L. Gang, Z. Haixuan, E. Linning, Z. Ling, L. Yu, and Z. Juming, “Recognition of honeycomb lung in CT images based on improved MobileNet model,” Med. Phys., vol. 48, no. 8, pp. 4304–4315, 2021, doi: 10.1002/mp.14873.
  • Z. Chen, J. Duan, L. Kang, and G. Qiu, “Class-Imbalanced Deep Learning via a Class-Balanced Ensemble,” IEEE Trans. Neural Networks Learn. Syst., vol. 33, no. 10, pp. 5626–5640, 2022, doi: 10.1109/TNNLS.2021.3071122.
  • N. Liu, X. Li, E. Qi, M. Xu, L. Li, and B. Gao, “A novel ensemble learning paradigm for medical diagnosis with imbalanced data,” IEEE Access, vol. 8, pp. 171263–171280, 2020, doi: 10.1109/ACCESS.2020.3014362.
  • V. López, A. Fernández, J. G. Moreno-Torres, and F. Herrera, “Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics,” Expert Syst. Appl., vol. 39, no. 7, pp. 6585–6608, Jun. 2012, doi: 10.1016/j.eswa.2011.12.043.
  • F. Jia, S. Li, H. Zuo, and J. Shen, “Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines under Imbalanced Data,” IEEE Access, vol. 8, pp. 120974–120982, 2020, doi: 10.1109/ACCESS.2020.3006895.
  • X. Yuan, L. Xie, and M. Abouelenien, “A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data,” Pattern Recognit., vol. 77, pp. 160–172, 2018, doi: 10.1016/j.patcog.2017.12.017.
  • A. S. Qureshi and T. Roos, “Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets,” Neural Process. Lett., vol. 55, no. 4, pp. 4461–4479, 2023, doi: 10.1007/s11063-022-11049-4.
  • M. N. Hossin, M., Sulaiman, “A Review of Evaluation Metrics in Machine Learning Algorithms,” Med. Image Anal., vol. 80, no. 2, p. 102478, 2022, [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S1361841522001256.
  • M. H. Al-Adhaileh, “Diagnosis and classification of Alzheimer’s disease by using a convolution neural network algorithm,” Soft Comput., vol. 26, no. 16, pp. 7751–7762, 2022, doi: 10.1007/s00500-022-06762-0.
  • S. Murugan et al., “DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia from MR Images,” IEEE Access, vol. 9, pp. 90319–90329, 2021, doi: 10.1109/ACCESS.2021.3090474.
  • M. M. S. Fareed et al., “ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans,” IEEE Access, vol. 10, no. September, pp. 96930–96951, 2022, doi: 10.1109/ACCESS.2022.3204395.
  • N. Goenka and S. Tiwari, “AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches,” Biomed. Signal Process. Control, vol. 74, no. January, p. 103500, 2022, doi: 10.1016/j.bspc.2022.103500.
  • M. Odusami, R. Maskeliūnas, and R. Damaševičius, “An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging,” Sensors, vol. 22, no. 3, 2022, doi: 10.3390/s22030740.
  • S. A. Kumar and S. Sasikala, “Enhanced Alzheimer’s Disease Classification Using Multilayer Deep Convolutional Neural Network-Based Experimentations,” Iran. J. Sci. Technol. - Trans. Electr. Eng., vol. 47, no. 4, pp. 1595–1621, 2023, doi: 10.1007/s40998-023-00622-9.
There are 45 citations in total.

Details

Primary Language English
Subjects Software Testing, Verification and Validation, Software Engineering (Other)
Journal Section Research Article
Authors

Nedim Muzoglu 0000-0003-1591-2806

Enver Akbacak 0000-0002-6753-7887

Early Pub Date December 23, 2024
Publication Date December 31, 2024
Submission Date May 31, 2024
Acceptance Date September 30, 2024
Published in Issue Year 2024Volume: 7 Issue: 3

Cite

IEEE N. Muzoglu and E. Akbacak, “Ensemble-Based Alzheimer’s Disease Classification Using Features Extracted from Hog Descriptor and Pre-trained Models”, SAUCIS, vol. 7, no. 3, pp. 416–426, 2024, doi: 10.35377/saucis...1493368.
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