Feature extraction and classification of Dementia with neural network

Akhila, J A; Markose, Christin; Aneesh, R P

doi:10.1109/icicict1.2017.8342782

Cited by 19 publications

(6 citation statements)

References 7 publications

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“…The performance of AI solutions in studying dementia has been extensively studied. Applications of AI in accurately diagnosing dementia and classifying it into subtypes have been compared with doctors' diagnoses [22][23][24][25][26][27]. Correlation analyses have been conducted in the segmentation area using programs such as arterial spin labeling, FreeSurfer [28], or manual segmentation [29].…”

Section: Discussionmentioning

confidence: 99%

Improving performance robustness of subject-based brain segmentation software

Park¹,

Park

Kim³

et al. 2023

encephalitis

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PurposeArtificial intelligence (AI)-based image analysis tools to quantify the brain have become commercialized. However, insufficient data for learning and scanner specificity is a limitation for achieving high quality. In the present study, the performance of personalized brain segmentation software when applied to multicenter data using an AI model trained on data from a single institution was improved.MethodsPreindicators of brain white matter (WM) information from the training dataset were utilized for preprocessing. During learning, data of cognitively normal (CN) individuals from a single center were utilized, and data of CN individuals and Alzheimer disease (AD) patients enrolled in multiple centers were considered the test set.ResultsThe preprocessing based on the preindicator (dice similarity coefficient [DSC], 0.8567) resulted in a better performance than without (DSC, 0.7921). The standard deviation (SD) of the WM region intensity (DSC, 0.8303) had a more substantial influence on the performance than the average intensity (DSC, 0.6591). When the SD of the test data WM intensity was smaller than the learning data, the performance improved (0.03 increase in lower SD, 0.05 decrease in higher SD). Furthermore, preindicator-based pretreatment increased the correlation of mean cortical thickness of the entire gray matter between Atroscan and FreeSurfer, and data augmentation without preprocessing did not.Both preindicator processing and data augmentation improved the correlation coefficient from 0.7584 to 0.8165.ConclusionData augmentation and preindicator-based preprocessing of training data can improve the performance of AI-based brain segmentation software, both increasing the generalizability and stability of brain segmentation software.

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Section: Discussionmentioning

confidence: 99%

Improving performance robustness of subject-based brain segmentation software

Park¹,

Park

Kim³

et al. 2023

encephalitis

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“…An artificial neural network (ANN) is one of the constructions of deep learning and has been applied for a diversity of tasks comprising medical image processing. In [94], a twolayered feed-forward neural network designed with sigmoid function is trained using the Levenberg-Marquardt supervised algorithm to classify the images as normal or demented. The Two-Threshold Binary Decomposition algorithm breaks down the input grayscale image into binary images.…”

Section: B Deep Learning Approaches For Dementia Diagnosismentioning

confidence: 99%

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Ahmed

Zhang

Feng

et al. 2019

IEEE Rev. Biomed. Eng.

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Dementia, a chronic and progressive cognitive declination of brain function due to disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving the accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. Much of the success of these approaches can be attributed to the application of machine learning and deep learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the previous five years (2013-2018). Based on the rigorous review of the existing works, we have found that while most of the studies focused on Alzheimer's disease have demonstrated reasonable performance, the identification of other types of dementia remains a major challenge. Hybrid imaging analysis through deep learning approaches may hold promises for early diagnosis of these other types of dementia, and warrant further investigation in the future. The main contributions of this review paper are as follows: 1. Based on the detailed analysis of the existing literature, this paper discusses the most recent neuroimaging procedures for dementia diagnosis, and 2. It systematically explains the machine learning techniques and, in particular, deep learning approaches for early detection of dementia.

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“…Contrast Enhancement methods of the image can be divided into two techniques: Direct method and Indirect method. Considering indirect methods, Histogram Equalization which is a simple and explicit approach is mostly used [3]. Contrast enhancement alters input image's pixel intensity to employ maximum bins or as many bins as possible.…”

Section: Image Pre-processingmentioning

confidence: 99%

Detection of Frontotemporal Dementia using Artificial Intelligence Techniques

N.*,

Prasath. A.

2019

IJRTE

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Atrophy (degeneration) of frontal and temporal lobes in human brain causes a dysfunctionality in Language, Emotion, Executive abilities and these losses are irreversible. If Atrophy is detected at an early age it is beneficial for the patient. A detailed diagnosis of the patient in terms of physical, physiological, psychological and neuropsychological aspects are mandatory to trace the possibility. There is a continuous increase in population of the disease and limited number of specialists. Frontotemporal Dementia (FTD) happens due to the frontal and/or temporal lobe degeneration. The proposed system helps in the identification of the FTD in the brain using various Artificial Intelligence (AI) techniques. The study makes use of the MR brain images for the purpose of the detection of the FTD.

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Feature extraction and classification of Dementia with neural network

Cited by 19 publications

References 7 publications

Improving performance robustness of subject-based brain segmentation software

Improving performance robustness of subject-based brain segmentation software

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Detection of Frontotemporal Dementia using Artificial Intelligence Techniques

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