Efficient optimization based thresholding technique for analysis of alzheimer MRIs

Sundaravadivel, Prabha; Sankaran, Kris; Chitradevi, D

doi:10.1080/00207454.2021.1901696

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…The MRI images of brain tumors are three-dimensional, and different types of brain tumors showed different characteristics and presented different states. The manual segmentation of brain tumor images is a heavy and time-consuming task, so it is the current trend to study the brain tumor image segmentation [ 17 ]. The outcome of clustering coincides with the goal that people want to achieve using image segmentation, so clustering algorithms are widely used in image segmentation [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C‐Means Algorithm

Liu

Zhang

Niu

et al. 2022

Contrast Media & Molecular Imaging

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This study was aimed to explore the diagnostic value of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in brain tumors under the fuzzy C-means (FCM) algorithm. The two-dimensional FCM hybrid algorithm was improved to be three-dimensional. The MRI images and MRS spectra of 127 patients with brain tumors (low-grade glioma group) and 54 healthy people (healthy group) were analyzed. The results suggested that the membership matrix of the improved algorithm had lower ambiguity, higher segmentation accuracy, closer relationship of intrapixels, and stronger irrelevance of interclass pixels. Through the analysis of gray matter volume, it was found that, compared with the healthy group, the gray matter and white matter volumes in the brain of high-grade glioma were higher, and those of low-grade glioma group were lower. The improved FCM algorithm could obtain a higher accuracy of 88.64% in segmenting images. It had a higher sensitivity to gray matter changes in brain tumors, reaching 92.72%; its specificity was not much different from that of traditional FCM, which were 83.61% and 88.06%, respectively. In the diagnostic value, the area under the curve of mean kurtosis was the largest, which was 0.962 ( P < 0.001). The best critical value was 0.4096, which had a greater reference significance for clinical treatment and prognosis. The ratio of choline/N-acetyl-aspartate and the ratio of choline/creatine also showed significant differences in high- and low-grade gliomas ( P < 0.05), but the specificity and sensitivity were slightly lower. It also had guiding significance for the grading of gliomas. Overall, the improved FCM algorithm had obvious advantages in the segmentation process of MRI images, which provided help for the clinical diagnosis of brain tumors.

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Section: Discussionmentioning

confidence: 99%

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C‐Means Algorithm

Liu

Zhang

Niu

et al. 2022

Contrast Media & Molecular Imaging

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“…The rate at which this disease progresses varies from patient to patient, but it has a terrible outcome. It produces a behavioral abnormality that affects the patient's social functionality [11][12][13]. The normal onset and signs of this illness appear beyond the age of 65, however, it can develop earlier in life and the symptoms may not appear until this age.…”

Section: Introductionmentioning

confidence: 99%

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

M.¹,

Rao²

2023

Computers, Materials &Amp; Continua

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Neurological disorders such as Alzheimer's disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into five classes. Initially, a network based on Unet++ is built to segment the tissues of the brain. Then, using the segmented tissue components as input, the Xception-based deep learning technique is employed to extract high-level features. Finally, the optimized Fractalnet framework is used to categorize the disease condition using the acquired characteristics. The proposed strategy is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset that accurately segments brain tissues with a 98.45% of dice similarity coefficient (DSC). Additionally, for the multiclass classification of AD, the suggested technique obtains an accuracy of 99.06%. Moreover, ANOVA statistical analysis is also used to evaluate if the groups are significant or not. The findings show that the suggested model outperforms various stateof-the-art methods in terms of several performance metrics.

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“…The Dementia is a type of brain disease which causes memory loss that affects the brain and its initial stage is Alzheimer's, which causes memory, cognitive, and behavioral issues [1]. Dementia symptoms may not be severe in the early stages, it becomes severe as the disease damages the brain.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques in Classification of Stages in Dementia: An Ensemble Approach

Pandiyarajan Mariappan,

R. S. Valarmathi

2023

ARASET

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Dementia is the severe brain disease that affects the memory system of the humans which requires early diagnosis. It progresses through different stages from very mild dementia, which motivates to find the stage of dementia for treating the affected persons. A novel ensemble-based classification model to classify the stages in dementia such as mild-dementia, non-dementia, very-mild dementia has been proposed in this work. The MRI images were pre-processed with techniques such as resizing, data augmentation and Contrast Limited Adaptive Histogram Equalization (CLAHE) and different four forms of datasets such as Image Data-1, Image Data-2, Image Data-3, Image Data-4 are framed to justify preprocessing techniques. The pre-trained models namely ResNet, Inception, MobileNet, VGG, DenseNet, Xception, Inception Resnet, AlexNet, EfficientNet and NasNet Mobile are trained with all four forms of dataset and performance of each model is analysed. The models DenseNet201, MobileNetV2, VGG19, ResNet152 achieved accuracy of 93%,93%,93%.90% respectively with Image Data-4. The proposed model is the ensemble of DenseNet201, MobileNetV2, VGG19, ResNet152 which provides accuracy precision, recall and f1-score of 95%, 93%, 100% and 96% respectively. This ensemble model has undergone testing using a brain tumour dataset, and the experimental findings indicate that the proposed framework enhances the classifier's generalization capabilities for multiclass problems.

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Efficient optimization based thresholding technique for analysis of alzheimer MRIs

Cited by 12 publications

References 42 publications

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C‐Means Algorithm

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C‐Means Algorithm

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

Deep Learning Techniques in Classification of Stages in Dementia: An Ensemble Approach

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