Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis

Rallabandi, V. P. Subramanyam; Tulpule, Ketki; Gattu, Mahanandeeshwar

doi:10.1016/j.imu.2020.100305

Cited by 84 publications

(43 citation statements)

References 38 publications

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“…Data type AUC [51] sMRI 0.8722 [52] sMRI 0.861 [53] sMRI 0.76 [57] SNPs (482) 0.842 [16] SNPs (2500) 0.719 [58] SNPs (11) 0.8949 MRI 0.993 [59] sMRI 0.9252 [60] amyloid PET 0.908 [61] amyloid PET + MRI 0.9234 [63] SNPs (200) 0.62 [17] SNPs (20 & 50) 0.68 [18] SNPs (41) 0.6807 [19] SNPs (20) 0.689 *Note: images data results were measured by ACC journal.ump.edu.my/ijsecs ◄ Furthermore, when the results of ML and DL based approaches were compared in terms of genetic variants data and neuroimaging data, shown Table 3, it was found that DL based approaches had achieved better performance in Neuroimaging data compared to ML based approaches, while they were relatively poor with SNPs data.…”

Section: Table 1 ML Approaches Results With Mri and Snps Data Ref Nomentioning

confidence: 99%

“…The SVM with radial basis function (RBF) kernel attained the best outcomes with AUC of 0.861. Moreover, Researchers in [53] utilized 1,167 sMRI scans to classify normal cognitive (NC) state and three different states of dementia: early MCI, late MCI, and probable AD. The approach trained six ML classifiers: KNN, Decision tree, RF, Naïve Bayes (NB), linear SVM and nonlinear SVM with RBF kernel.…”

Section: Magnetic Resonance Imaging (Mri) Datamentioning

confidence: 99%

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Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It can cause a massive impact on a patient's memory and mobility. As this disease is irreversible, early diagnosis is crucial for delaying the symptoms and adjusting the patient's lifestyle. Many machine learning (ML) and deep learning (DL) based-approaches have been proposed to accurately predict AD before its symptoms onset. However, finding the most effective approach for AD early prediction is still challenging. This review explored 24 papers published from 2018 until 2021. These papers have proposed different approaches using state of the art machine learning and deep learning algorithms on different biomarkers to early detect AD. The review explored them from different perspectives to derive potential research gaps and draw conclusions and recommendations. It classified these recent approaches in terms of the learning technique used and AD biomarkers. It summarized and compared their findings, and defined their strengths and limitations. It also provided a summary of the common AD biomarkers. From this review, it was found that some approaches strove to increase the prediction accuracy regardless of their complexity such as using heterogeneous datasets, while others sought to find the most practical and affordable ways to predict the disease and yet achieve good accuracy such as using audio data. It was also noticed that DL based-approaches with image biomarkers remarkably surpassed ML based-approaches. However, they achieved poorly with genetic variants data. Despite the great importance of genetic variants biomarkers, their large variance and complexity could lead to a complex approach or poor accuracy. These data are crucial to discover the underlying structure of AD and detect it at early stages. However, an effective pre-processing approach is still needed to refine these data and employ them efficiently using the powerful DL algorithms.

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Section: Table 1 ML Approaches Results With Mri and Snps Data Ref Nomentioning

confidence: 99%

Section: Magnetic Resonance Imaging (Mri) Datamentioning

confidence: 99%

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

View full text Add to dashboard Cite

show abstract

“…The SVM with a 2-degree polynomial kernel was used to classify AD. The proposed approach in [54] was developed to predict AD and MCI early and classify them from elderly cognitively normal. To compute CT of several anatomical regions from segmented gray matter tissue, the FreeSurfer method was used and required features were extracted.…”

Section: ) Ml-based Approaches In Ad Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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“…Computer aided mathematical biomedical image texture analysis provides an aid to radiology by interpreting the image in terms of statistical features and signal variation algorithms giving a quantitative definition of image. List of latest texture based studies [18]- [24] on Brain atrophy MRI are listed in Table 1A.…”

Section: Literature Reviewmentioning

confidence: 99%

Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline

Jain¹,

S.²,

Jain³

et al. 2021

IJACSA

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Brain atrophy is the degradation of brain cells and tissues to the extent that it is clearly indicative during Mini-Mental State Exam test and other psychological analysis. It is an alarming state of the human brain that progressively results in Alzheimer disease which is not curable. But timely detection of brain atrophy can help millions of people before they reach the state of Alzheimer. In this study we analyzed the longitudinal structural MRI of older adults in the age group of 42 to 96 of OASIS 3 Open Access Database. The nth slice of one subject does not match with the nth slice of another subject because the head position under the magnetic field is not synchronized. As a radiologist analyzes the MRI image data slice wise so our system also compares the MRI images slice wise, we deduced a method of slice by slice registration by driving mid slice location in each MRI image so that slices from different MRI images can be compared with least error. Machine learning is the technique which helps to exploit the information available in abundance of data and it can detect patterns in data which can give indication and detection of particular events and states. Each slice of MRI analyzed using simple statistical determinants and Gray level Co-Occurrence Matrix based statistical texture features from whole brain MRI images. The study explored varied classifiers Support Vector Machine, Random Forest, K-nearest neighbor, Naive Bayes, AdaBoost and Bagging Classifier methods to predict how normal brain atrophy differs from brain atrophy causing cognitive impairment. Different hyper parameters of classifiers tuned to get the best results. The study indicates Support Vector Machine and AdaBoost the most promising classifier to be used for automatic medical image analysis and early detection of brain diseases. The AdaBoost gives accuracy of 96.76% with specificity 95.87% and sensitivity 87.37% and receiving operating curve accuracy 96.3%. The SVM gives accuracy of 96% with 92% specificity and 87% sensitivity and receiving operating curve accuracy 95.05%.

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Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis

Cited by 84 publications

References 38 publications

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline

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