Classification of Magnetic Resonance Images using Bag of Features for Detecting Dementia

Bansal, Deepika; Khanna, Kavita; Chhikara, Rita; Dua, Rakesh Kumar; Malhotra, Rajeev

doi:10.1016/j.procs.2020.03.190

Cited by 18 publications

(12 citation statements)

References 18 publications

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“…Multiple Kernel Learning (MKL) was used to combine these features with the amount of Cerebrospinal Fluid (CSF). They reported in their previous study 24 applied to sMRI images that GL-CHFs are more efficient than scale-invariant feature transform (SIFT) and Speeded-Up Robust Features (SURF) local descriptors in contrast to Bansal et al 25 These authors have reached an accuracy of 93% using bag of Feature (BoF) witch SURF and SVM classifier.…”

Section: Related Workmentioning

confidence: 99%

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Houria

Belkhamsa

Cherfa

et al. 2022

Int J Imaging Syst Tech

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Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs.The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.

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Section: Related Workmentioning

confidence: 99%

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Houria

Belkhamsa

Cherfa

et al. 2022

Int J Imaging Syst Tech

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show abstract

“…Most of the studies performed on the prediction of dementia using machine learning models did not mention a clear methodology that could be generalized to ensure a stable high-performance prediction independently of the underlying dataset (Bansal et al, 2020;Battineni et al, 2020;Bharanidharan & Rajaguru, 2020;Dallora et al, 2020;Popuri et al, 2020;Sharma et al, 2020;You et al, 2019). This is because each study uses a different dataset with various features from different sources.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, it was found that the proposed hybrid model provides enhanced accuracy of dementia prediction up to 98%. In another study, a machine learning model was presented for the classification of dementia disease using magnetic resonance imaging (Bansal et al, 2020). The researchers proposed using the bag of features method to extract the features of magnetic resonance imaging scans in association with a support vector machine to classify these scans.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This brain disorder is often accompanied by memory attenuation. From healthcare records, it was found that people aged 65 and above are more vulnerable to disease (Bansal et al, 2018(Bansal et al, , 2020Lakshmi, 2020). According to the World Health Organization (WHO), there are around 50 million patients with dementia worldwide with an increase of 10 million patients annually (Battineni et al, 2020;Harvey et al, 2003;WHO, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Examining stability of machine learning methods for predicting dementia at early phases of the disease

Faouri¹,

AlBashayreh²,

Azzeh³

2022

10.5267/j.dsl

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Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients’ lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia. These promising results open the door to a new era of early prognosis of Alzheimer’s Disease and Related Dementias (ADRD).

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“…Various cognitive, clinical and genetic tests are available for diagnosing dementia. MRI images are utilized for diagnosing the disease, as they hold a connection with the topology of the brain, and also, the modifications in the morphological structure of the brain are easily visible (Bansal et al, 2020; Puente‐Castro et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A superpixel powered autoencoder technique for detecting dementia

et al. 2021

Self Cite

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Dementia is a neurocognitive disorder responsible for decreasing the overall quality of life for patients. The disease has emerged as a worldwide health challenge in adults in the age group of 65 years or above. Deep learning has been successfully applied for the prediction of dementia using magnetic resonance imaging. In this paper, a superpixel-powered autoencoder technique has been proposed using a histogram of oriented gradients for extracting the relevant features. The proposed technique is capable of predicting and classifying three categories of dementia-normal, mild cognitive impairment and dementia subjects. The viability of the proposed method is established by comparing it with the other state of art models and the popular pretrained networks including Squeezenet, Resnet50, Resnet18, Inceptionv3, Googlenet, VGG19 and Alexnet. The experimental results establish that the proposed model has performed significantly better than the state of art models and has outperformed the popular pre-trained networks.

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Classification of Magnetic Resonance Images using Bag of Features for Detecting Dementia

Cited by 18 publications

References 18 publications

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Examining stability of machine learning methods for predicting dementia at early phases of the disease

A superpixel powered autoencoder technique for detecting dementia

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