A review of machine learning techniques for diagnosing Alzheimer’s disease using imaging modalities

被引：0
作者：
Nand Kishore [1 ]
Neelam Goel [1 ]
机构：
[1] Department of Information Technology, University Institute of Engineering and Technology, Panjab University, Chandigarh
来源：
Neural Computing and Applications | 2024年 / 36卷 / 35期
关键词：
Alzheimer’s disease; Convolutional neural networks; Machine learning; Multi-modality;
D O I：
10.1007/s00521-024-10399-5
中图分类号：
学科分类号：
摘要：
Alzheimer's disease is a progressive form of dementia. Dementia is a broad term for conditions that impair memory, thinking, and behaviour. Brain traumas or disorders can cause dementia. It is estimated that 60–80% of dementia cases around the world are caused by Alzheimer’s disease, an incurable neurodegenerative disorder. Although Alzheimer's disease research has increased in recent years, early diagnosis is challenging due to the complicated brain structure and functions associated with this disease. It is difficult for doctors to identify Alzheimer's disease in its early stages as there are still no biomarkers to be precise in early detection. In the area of medical imaging, deep learning is becoming increasingly popular and successful. There is no single best approach for the detection of Alzheimer's disease. In comparison with conventional machine learning methods, the deep learning models detect Alzheimer's disease more precisely and effectively. In this review paper, various machine learning-based techniques utilized for the classification of Alzheimer's disease through different imaging modalities are discussed. In addition, a comprehensive and detailed analysis of the various image processing procedures along with corresponding classification performance and feature extraction techniques have been meticulously compiled and presented. The investigation of computer-aided image analysis has demonstrated significant potential in the early detection of cognitive changes in individuals experiencing mild cognitive impairment. Machine learning can provide valuable insights into the cognitive status of patients, enabling healthcare professionals to intervene and provide timely treatment. This review may lead to a reliable method for recognizing and predicting Alzheimer's disease. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
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页码：21957 / 21984
页数：27
相关论文
共 88 条
  • [1] Ozsahin I., Sekeroglu B., Mok G.S.P., The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer’s disease using Alzheimer’s Disease Neuroimaging Initiative database, PLoS ONE, 14, pp. 1-13, (2019)
  • [2] Ding Y., Sohn J.H., Kawczynski M.G., Et al., A deep learning model to predict a diagnosis of Alzheimer disease by using 18 F-FDG PET of the brain, Radiology, 290, pp. 456-464, (2019)
  • [3] Ramzan F., Khan M.U.G., Rehmat A., Et al., A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks, J Med Syst, (2020)
  • [4] Porsteinsson A.P., Isaacson R.S., Knox S., Et al., Diagnosis of early Alzheimer’s disease: clinical practice in 2021, J Prev Alzheimers Dis, 8, pp. 371-386, (2021)
  • [5] Zhang F., Li Z., Zhang B., Du H., Wang B., Zhang X., Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease, Neurocomputing, (2019)
  • [6] Saleem T.J., Zahra S.R., Wu F., Et al., Deep learning-based diagnosis of Alzheimer’s disease, J Pers Med, (2022)
  • [7] World Alzheimer report 2021, Alzheimer’s Dis Int, pp. 2-314, (2021)
  • [8] Raghavaiah P., Varadarajan S., A CAD system design for Alzheimer’s disease diagnosis using temporally consistent clustering and hybrid deep learning models, Biomed Signal Process Control, 75, (2022)
  • [9] Mirzaei G., Adeli H., Machine learning techniques for diagnosis of Alzheimer disease, mild cognitive disorder, and other types of dementia, Biomed Signal Process Control, 72, (2022)
  • [10] Zhao X., Ang C.K.E., Acharya U.R., Cheong K.H., Application of artificial intelligence techniques for the detection of Alzheimer’s disease using structural MRI images, Biocybern Biomed Eng, 41, pp. 456-473, (2021)
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