Alzheimer’S Disease Classification Using Bag-Of-Words Based On Visual Pattern Of Diffusion Anisotropy For DTI Imaging

Eldeeb, Ghaidaa W.; Zayed, Nourhan; Yassine, Inas A.

doi:10.1109/embc.2018.8512203

Cited by 6 publications

(10 citation statements)

References 11 publications

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“…Studies reported in this review show evidence that automated DTI-based classifications of both MCI/HC and MCI/AD provide considerably inferior results than AD/HC separation (accuracy: ~80%). Only two studies obtained an accuracy higher than 90% [46,52], but also in this case, the limited sample size needs to be considered as a potential bias (Figure 4). Lower accuracy in these classifications is probably due to less marked differences between the features extracted.…”

Section: Discussionmentioning

confidence: 98%

“…As regards the binary classification between AD and HC, very high performance in terms of accuracy (>90%) was achieved by several studies ( [35,37,39,41,43,46,52]), among which, two even obtained 100% accuracy ( [35,46]) (Figure 3). However, it should be noted that the sample size of these studies, in particular of the ones obtaining an accuracy of 100%, is quite limited (15-35 subjects per group), thus, the model could have been overfitted and could lack generalizability.…”

Section: Discussionmentioning

confidence: 99%

“…Only one work [52] investigated the ternary problem: AD vs. MCI vs. HC and reached a good performance (accuracy = 89%). Thus, from this study, it seems that the integration of DTI with ML can be a variable instrument for the AD vs. MCI vs. HC classification also in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…Concerning the classification approach, it can be observed that SVM was the most frequently adopted method both for the classification of only AD [35,37,[39][40][41][42][43]45] or also MCI [46][47][48][49]52,54,55] classification (Figures 3 and 4). Linear discriminant analysis [44,45,55] or naïve Bayes [40,46] were also sometimes used in AD classification.…”

Section: Discussionmentioning

confidence: 99%

“…Eldeeb et al [52] proposed a novel method to extract relevant markers associated with FA and MD. After preprocessing of DTI-data, FA and MD maps of regions of interest were determined using a "bag-of-words" model.…”

Section: Ad/mci/hc Classificationmentioning

confidence: 99%

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Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

et al. 2020

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Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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Section: Discussionmentioning

confidence: 98%