Deep ensemble learning for intelligent healthcare computing: A case study of Alzheimer’s disease

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Published Aug 9, 2024
https://doi.org/10.47164/ijngc.v15i2.1475
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Volume 15, Issue 2, July 2024

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Tawseef Ayoub Shaikh
National Institute of Technology (NIT), Srinagar, Jammu and Kashmir, India-190006
Tawqeer Ul Islam
National Institute of Technology (NIT), Srinagar, Jammu and Kashmir, India-190006
Sameen Rafi Mir
Government College of Engineering & Technology, Chak Bhalwal, Jammu, Jammu & Kashmir, India-181122
Tsewang Namgail
Government College of Engineering & Technology, Chak Bhalwal, Jammu, Jammu & Kashmir, India-181122
Inam Ul Haq Gulzar
Government College of Engineering & Technology, Chak Bhalwal, Jammu, Jammu & Kashmir, India-181122

Abstract

The growing popularity of deep learning (DL) in recent years has encouraged researchers to diversify their applications further. The limitations and shortcomings of an individual model are subdued through ensemble learning (EL), which combines the predictions of multiple models that are trained separately, thereby improving the overall accuracy and robustness. Deep ensemble learning (DEL) models leverage the combined diversity of different deep learning models. This paper provides an overview of traditional, novel, and state-of-the-art deep ensemble methods for application in Alzheimer's disease (AD) and other intelligent healthcare applications, including bagging, boosting, stacking, homogeneous/heterogeneous ensembles, explicit/implicit ensembles, negative correlation-based deep ensemble models and decision fusion. For this research study, an extensive exploration was conducted across prominent academic databases, including Google Scholar, ProQuest, DBLP, Science Direct, MDPI, IEEE Xplore, and Springer. The investigation encompassed a meticulous search for literature between 2018 and 2023 to ascertain the study's most current and relevant data. The results are presented through various methodologies, including flow charts, graphs, figures, and comparative tables, ensuring a comprehensive and visually accessible representation of the findings. This survey paper presents performance results from diverse ensemble methods applied to deep learning models. This reveals significant performance enhancements on specific datasets and model combinations, showcasing the impactful role of ensembling in surpassing individual model outcomes. Our findings also highlight nuanced correlations between ensemble techniques and data characteristics, offering actionable insights for implementing optimized ensemble-based deep learning models in clinical settings. This novel contribution underscores our paper's advancement in Alzheimer's detection methodologies, uniting comprehensive data analysis, ensemble effectiveness, and valuable considerations.

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How to Cite
Shaikh, T. A., Islam, T. U. ., Mir, S. R., Namgail, T. ., & Gulzar, I. U. H. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2024). Deep ensemble learning for intelligent healthcare computing: A case study of Alzheimer’s disease. International Journal of Next-Generation Computing, 15(2). https://doi.org/10.47164/ijngc.v15i2.1475
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References

  1. Ahmed, S., Choi, K. Y., Lee, J. J., Kim, B. C., Kwon, G.-R., Lee, K. H., and Jung, H. Y. 2019. Ensembles of patch-based classifiers for diagnosis of Alzheimer diseases.IEEE Access 7, 73373–73383. DOI: https://doi.org/10.1109/ACCESS.2019.2920011
  2. Alsekait, D. M., Saleh, H., Gabralla, L. A., Alnowaiser, K., El-Sappagh, S., Sahal, R., and El-Rashidy, N. 2023. Toward comprehensive chronic kidney disease prediction based on ensemble deep learning models. Applied Sciences 13, 6, 3937. DOI: https://doi.org/10.3390/app13063937
  3. Al-Shoukry, S., Rassem, T. H., and Makbol, N. M. 2020. Alzheimer’s diseases detection by using deep learning algorithms: A mini-review. IEEE Access 8, 77131–77141. DOI: https://doi.org/10.1109/ACCESS.2020.2989396
  4. Baccouche, A., Garcia-Zapirain, B., Castillo Olea, C., and Elmaghraby, A. 2020. Ensemble deep learning models for heart disease classification: A case study from Mexico. Information 11, 4, 207. DOI: https://doi.org/10.3390/info11040207
  5. Balaji, P., Chaurasia, M. A., Bilfaqih, S. M., Muniasamy, A., and Alsid, L. E. G. 2023. Hybridized Deep Learning Approach for detecting Alzheimer’s disease. Biomedicines 11, 1, 149. DOI: https://doi.org/10.3390/biomedicines11010149
  6. Balasundaram, A., Srinivasan, S., Prasad, A., Malik, J., and Kumar, A. 2023. Hippocampus segmentation-based Alzheimer’s disease diagnosis and classification of MRI images. Arabian Journal for Science and Engineering 48, 8, 1–17. DOI: https://doi.org/10.1007/s13369-022-07538-2
  7. Cha, D., Pae, C., Seong, S.-B., Choi, J. Y., and Park, H.-J. 2019. Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database. EBioMedicine 45, 606–614. DOI: https://doi.org/10.1016/j.ebiom.2019.06.050
  8. De Silva, K., and Kunz, H. 2023. Prediction of Alzheimer’s disease from magnetic resonance imaging using a convolutional neural network. Intelligence-Based Medicine 7, 100091. DOI: https://doi.org/10.1016/j.ibmed.2023.100091
  9. Deture, M. A., and Dickson, D. W. 2019. The neuropathological diagnosis of Alzheimer’s disease. Molecular Neurodegeneration 14, 1, 32. DOI: https://doi.org/10.1186/s13024-019-0333-5
  10. Dua, M., Makhija, D., Manasa, P. Y. L., and Mishra, P. 2020. A CNN–RNN–LSTM based amalgamation for Alzheimer’s disease detection. Journal of Medical and Biological Engineering 40, 5, 688-706. DOI: https://doi.org/10.1007/s40846-020-00556-1
  11. Duc, N. T., Ryu, S., Qureshi, M. N. I., Choi, M., Lee, K. H., andLee, B. 2020. 3D-deep learning based automatic diagnosis of Alzheimer’s disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics 18, 1, 71–86. DOI: https://doi.org/10.1007/s12021-019-09419-w
  12. El Asnaoui, K. 2021. Design ensemble deep learning model for pneumonia disease classification. International Journal of Multimedia Information Retrieval 10, 1, 55–68. DOI: https://doi.org/10.1007/s13735-021-00204-7
  13. El-Sappagh, S., Abuhmed, T., Riazul Islam, S. M., and Kwak, K. S. 2020. Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data. Neurocomputing 412, 197–215. DOI: https://doi.org/10.1016/j.neucom.2020.05.087
  14. Fang, X., Liu, Z., and Xu, M. 2020. Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer’s disease diagnosis. IET Image Processing 14, 2, 318–326. DOI: https://doi.org/10.1049/iet-ipr.2019.0617
  15. Fathi, S., Ahmadi, M., and Dehnad, A. 2022. Early diagnosis of Alzheimer’s disease based on deep learning: A systematic review. Computers in Biology and Medicine 146, 105634. DOI: https://doi.org/10.1016/j.compbiomed.2022.105634
  16. Feng, C., Elazab, A., Yang, P., Wang, T., Zhou, F., Hu, H., Xiao, X. and Lei, B. 2019. Deep learning framework for Alzheimer’s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access 7, 63605–63618. DOI: https://doi.org/10.1109/ACCESS.2019.2913847
  17. Francis, A., and Pandian, I. A. 2023. Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging. TELKOMNIKA (Telecommunication Computing Electronics and Control) 21, 2, 374. DOI: https://doi.org/10.12928/telkomnika.v21i2.23352
  18. Fulton, L. V., Dolezel, D., Harrop, J., Yan, Y., and Fulton, C. P. 2019. Classification of Alzheimer’s disease with and without imagery using gradient boosted machines and ResNet-50. Brain Sciences 9, 9, 212. DOI: https://doi.org/10.3390/brainsci9090212
  19. Gamal, A., Elattar, M., and Selim, S. 2022. Automatic early diagnosis of Alzheimer’s disease using 3D deep ensemble approach. IEEE Access 10, 115974–115987. DOI: https://doi.org/10.1109/ACCESS.2022.3218621
  20. Ganaie, M. A., and Tanveer, M. 2022. Ensemble deep random vector functional link network using privileged information for Alzheimer’s disease diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  21. Gifani, P., Shalbaf, A., and Vafaeezadeh, M. 2021. Automated Detection of Covid-19 Using ensemble of transfer learning with deep convolutional neural network based on CT scans. International Journal of Computer Assisted Radiology and Surgery 16, 1, 115–123. DOI: https://doi.org/10.1007/s11548-020-02286-w
  22. Grassmann, F., Mengelkamp, J., Brandl, C., Harsch, S., Zimmermann, M.E., Linkohr, B., Peters, A., Heid, I.M., Palm, C. and Weber, B.H. 2018. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125, 9, 1410–1420. DOI: https://doi.org/10.1016/j.ophtha.2018.02.037
  23. Hazarika, R. A., Maji, A. K., Kandar, D., Jasinska, E., Krejci, P., Leonowicz, Z., and Jasinski, M. 2023. An approach for classification of Alzheimer’s disease using deep neural network and brain magnetic resonance imaging (MRI). Electronics 12, 3, 676. DOI: https://doi.org/10.3390/electronics12030676
  24. Hedayati, R., Khedmati, M., and Taghipour-Gorjikolaie, M. 2021. Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis. Biomedical Signal Processing and Control 66, 102397. DOI: https://doi.org/10.1016/j.bspc.2020.102397
  25. Hernandez, A. ´ , Panizo, A. ´ , and Camacho, D. 2019. An ensemble algorithm based on deep learning for tuberculosis classification. In Lecture Notes in Computer Science. 145–154. DOI: https://doi.org/10.1007/978-3-030-33607-3_17
  26. Islam, J., and Zhang, Y. 2018. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Informatics 5, 2. DOI: https://doi.org/10.1186/s40708-018-0080-3
  27. Jabason, E., Ahmad, M. O., and Swamy, M. N. S. 2019. Classification of Alzheimer’s disease from MRI data using an ensemble of hybrid deep convolutional neural networks. In Proceedings of the IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). 481-484. DOI: https://doi.org/10.1109/MWSCAS.2019.8884939
  28. Ji, H., Liu, Z., Yan, W. Q., and Klette, R. 2019. Early diagnosis of Alzheimer’s disease using deep learning. In Proceedings of the 2nd International Conference on Control and Computer Vision, 87-91. DOI: https://doi.org/10.1145/3341016.3341024
  29. Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., and Qian, W. 2019. An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2045-2048. DOI: https://doi.org/10.1109/EMBC.2019.8857160
  30. Kandel, I., Castelli, M., and Popovic, A. ˇ 2021. Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification. Journal of Imaging 7, 6, 100. DOI: https://doi.org/10.3390/jimaging7060100
  31. Kang, W., Lin, L., Zhang, B., Shen, X., and Wu, S. 2021. Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer’s disease diagnosis. Computers in Biology and Medicine 136, 104678. DOI: https://doi.org/10.1016/j.compbiomed.2021.104678
  32. Liu, Y., and Long, F. 2019. Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning. In Lecture Notes in Bioengineering, 113–121. DOI: https://doi.org/10.1007/978-981-15-0798-4_12
  33. Loddo, A., Buttau, S., and Di Ruberto, C. 2022. Deep learning based pipelines for Alzheimer’s disease diagnosis: A comparative study and a novel deep-ensemble method. Computers in Biology and Medicine 141, 105032. DOI: https://doi.org/10.1016/j.compbiomed.2021.105032
  34. Lu, D., Popuri, K., Ding, G. W., Balachandar, R., and Beg, M. F. 2018. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Medical Image Analysis 46, 26–34. DOI: https://doi.org/10.1016/j.media.2018.02.002
  35. Ma, D., Lu, D., Popuri, K., Wang, L., andBeg, M. F. 2020. Differential diagnosis of Frontotemporal dementia, Alzheimer’s disease, and normal aging using a multi-scale multi-type feature generative adversarial deep neural network on structural magnetic resonance images. Frontiers in Neuroscience 14, 853. DOI: https://doi.org/10.3389/fnins.2020.00853
  36. Mao, C., Li, A., Hu, J., Wang, P., Peng, D., Wang, J., and Sun, Y. 2022. Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model. Frontiers in Molecular Biosciences 9, 951432. DOI: https://doi.org/10.3389/fmolb.2022.951432
  37. Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., and Buckner, R. L. 2010. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience 22, 12, 2677–2684. DOI: https://doi.org/10.1162/jocn.2009.21407
  38. Menagadevi, M., Mangai, S., Madian, N., and Thiyagarajan, D. 2023. Automated prediction system for Alzheimer detection based on deep residual autoencoder and support vector machine. Optik 272, 170212. DOI: https://doi.org/10.1016/j.ijleo.2022.170212
  39. Muller, D., Soto-Rey, I., and Kramer, F. 2022. An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks. IEEE Access 10, 66467–66480. DOI: https://doi.org/10.1109/ACCESS.2022.3182399
  40. Nguyen, D., Nguyen, H., Ong, H., Le, H., Ha, H., Duc, N. T., and Ngo, H. T. 2022. Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer’s disease. IBRO Neuroscience Reports 13, 255–263. DOI: https://doi.org/10.1016/j.ibneur.2022.08.010
  41. Orlando, J. I., Prokofyeva, E., Del Fresno, M., and Blaschko, M. B. 2018. An ensemble deep learning based approach for red lesion detection in fundus images. Computer Methods and Programs in Biomedicine 153, 115–127. DOI: https://doi.org/10.1016/j.cmpb.2017.10.017
  42. Ortiz, A., Munilla, J., Gorriz, J. M. ´ , and Ram´ırez, J. 2016. Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s Disease. International Journal of Neural Systems 26, 7, 1650025. DOI: https://doi.org/10.1142/S0129065716500258
  43. Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., and Song, X. 2020. Early detection of Alzheimer’s disease using magnetic resonance imaging: A novel approach combining convolutional neural networks and ensemble learning. Frontiers in Neuroscience 14, 259. DOI: https://doi.org/10.3389/fnins.2020.00259
  44. Ragab, M., Albukhari, A., Alyami, J., and Mansour, R. F. 2022. Ensemble DeepLearning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and classification on ultrasound images. Biology 11, 3, 439. DOI: https://doi.org/10.3390/biology11030439
  45. Razzak, I., Naz, S., Ashraf, A., Khalifa, F., Bouadjenek, M. R., and Mumtaz, S. 2022. Mutliresolutional ensemble PartialNet for Alzheimer detection using magnetic resonance imaging data. International Journal of Intelligent Systems 37, 10, 6613–6630. DOI: https://doi.org/10.1002/int.22856
  46. Reddy, G. N., and Reddy, K. N. 2022. Boosting based Deep hybrid Framework for Alzheimer’s DOI: https://doi.org/10.1109/ICDCS54290.2022.9780736
  47. Disease classification using 3D MRI. In Proceedings of the 6th International Conference on Devices, Circuits and Systems (ICDCS).
  48. Rossini, P. M., Di Iorio, R., Vecchio, F., Anfossi, M., Babiloni, C., Bozzali, M., Bruni, A.C., Cappa, S.F., Escudero, J., Fraga, F.J. and Giannakopoulos, P. Guntekin, B. , Logroscino, G., Marra, C., Miraglia, F., Panza, F., Tecchio, F., PascualLeone, A. and Dubois, B. 2020. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clinical Neurophysiology 131, 6, 1287–1310. DOI: https://doi.org/10.1016/j.clinph.2020.03.003
  49. Ruiz, J., Mahmud, M., Modasshir, M., and Shamim Kaiser, M. 2020. 3D DenseNet ensemble in 4-way classification of Alzheimer’s disease. In Lecture Notes in Computer Science. 85–96. DOI: https://doi.org/10.1007/978-3-030-59277-6_8
  50. Sadat, S. U., Shomee, H. H., Awwal, A., Amin, S. N., Reza, M. T., and Parvez, M. Z. 2021. Alzheimer’s disease detection and classification using transfer learning technique and ensemble on convolutional neural networks. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC). DOI: https://doi.org/10.1109/SMC52423.2021.9659179
  51. Sethuraman, S. K., Malaiyappan, N., Ramalingam, R., Basheer, S., Rashid, M., and Ahmad, N. 2023. Predicting Alzheimer’s disease using deep neuro-functional networks with resting-state fMRI. Electronics 12, 4, 1031. DOI: https://doi.org/10.3390/electronics12041031
  52. Sharma, R., Goel, T., Tanveer, M., Suganthan, P. N., Razzak, I., and Murugan, R. 2023. Conv-eRVFL: Convolutional neural network based ensemble RVFL classifier for Alzheimer’s disease diagnosis. IEEE Journal of Biomedical and Health Informatics 27, 10, 4995–5003. DOI: https://doi.org/10.1109/JBHI.2022.3215533
  53. Shi, J., Zheng, X., Li, Y., Zhang, Q., and Ying, S. 2018. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE Journal of Biomedical and Health Informatics 22, 1, 173–183. DOI: https://doi.org/10.1109/JBHI.2017.2655720
  54. Sreelakshmi, S., Malu, G., Sherly, E., and Mathew, R. 2023. M-Net: An encoderdecoder architecture for medical image analysis using ensemble learning. Results in Engineering 17, 100927. DOI: https://doi.org/10.1016/j.rineng.2023.100927
  55. Suk, H.-I., and Shen, D. 2013. Deep learning-based feature representation for AD/MCI classification. In Lecture Notes in Computer Science, 583–590. DOI: https://doi.org/10.1007/978-3-642-40763-5_72
  56. Sukegawa, S., Fujimura, A., Taguchi, A., Yamamoto, N., Kitamura, A., Goto, R., Nakano, K., Takabatake, K., Kawai, H., Nagatsuka, H. and Furuki, Y. 2022. Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates. Scientific Reports 12, 6088. DOI: https://doi.org/10.1038/s41598-022-10150-x
  57. Tanveer, M., Rashid, A. H., Ganaie, M. A., Reza, M., Razzak, I., and Hua, K.-L. 2022. Classification of Alzheimer’s disease using ensemble of deep neural networks trained through transfer learning. IEEE Journal of Biomedical and Health Informatics 26, 4, 1453–1463. DOI: https://doi.org/10.1109/JBHI.2021.3083274
  58. Taylor, A., Zhang, F., Niu, X., Heywood, A., Stocks, J., Feng, G., Popuri, K., Beg, M.F., and Wang, L. 2022. Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer’s Disease related neurodegeneration. NeuroImage 263, 119621. DOI: https://doi.org/10.1016/j.neuroimage.2022.119621
  59. Vasile, C.M., Udris, toiu, A.L., Ghenea, A.E., Popescu, M., Gheonea, C., Niculescu, C.E., Ungureanu, A.M., Udris, toiu, S, ., Drocas¸, A.I., Gruionu, L.G. and Gruionu, G. 2021. Intelligent diagnosis of thyroid ultrasound imaging using an ensemble of deep learning methods. Medicina 57, 4, 395. DOI: https://doi.org/10.3390/medicina57040395
  60. Venugopalan, J., Tong, L., Hassanzadeh, H. R., and Wang, M. D. 2021. Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific Reports 11, 3254. DOI: https://doi.org/10.1038/s41598-020-74399-w
  61. Wang, H., Shen, Y., Wang, S., Xiao, T., Deng, L., Wang, X., and Zhao, X. 2019. Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing 333, 145–156. DOI: https://doi.org/10.1016/j.neucom.2018.12.018
  62. Wang, S., Wang, H., Cheung, A. C., Shen, Y., and Gan, M. 2020. Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. In Advances in Intelligent Systems and Computing, 53–73. DOI: https://doi.org/10.1007/978-981-15-1816-4_4
  63. World Health Organization. Global status report on the public health response to dementia. 2021. Geneva.
  64. Yao, Z., Mao, W., Yuan, Y., Shi, Z., Zhu, G., Zhang, W., Wang, Z. and Zhang, G. 2023. Fuzzy-VGG: A fast deep learning method for predicting the staging of Alzheimer’s disease based on brain MRI. Information Sciences 642, 119129. DOI: https://doi.org/10.1016/j.ins.2023.119129
  65. Zhang, F., Li, Z., Zhang, B., Du, H., Wang, B., and Zhang, X. 2019. Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease. Neurocomputing 361, 185–195. DOI: https://doi.org/10.1016/j.neucom.2019.04.093
  66. Zhang, P., Lin, S., Qiao, J., and Tu, Y. 2021. Diagnosis of Alzheimer’s disease with ensemble learning classifier and 3D convolutional neural network. Sensors 21, 22, 7634. DOI: https://doi.org/10.3390/s21227634