Multi-class Alzheimer's disease classification using image and clinical features

Altaf, Tooba; Anwar, Syed Muhammad; Gul, Nadia; Majeed, Muhammad Nadeem; Majid, Muhammad

doi:10.1016/j.bspc.2018.02.019

Cited by 121 publications

(68 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are various methods reported in literature related to brain diseases that are based on both conventional features and DL based methods [7] [24] [25] [15] [9]. In [58], authors combines fully convolutional neural network with conditional random field (CRF).…”

Section: Radiomics Using Deep Learningmentioning

confidence: 99%

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

Anwar

Altaf

Rafique

et al. 2020

Radiomics and Radiogenomics in Neuro-Oncology

Self Cite

View full text Add to dashboard Cite

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistance in diagnosis of cancer, planning of treatment strategy, and prediction of survival. Radiomics in neuro-oncology has progressed significantly in the recent past. Deep learning has outperformed conventional machine learning methods in most image-based applications. Convolutional neural networks (CNNs) have seen some popularity in radiomics, since they do not require hand-crafted features and can automatically extract features during the learning process. In this regard, it is observed that CNN based radiomics could provide state-of-the-art results in neuro-oncology, similar to the recent success of such methods in a wide spectrum of medical image analysis applications. Herein we present a review of the most recent best practices and establish the future trends for AI enabled radiomics in neuro-oncology.

show abstract

Section: Radiomics Using Deep Learningmentioning

confidence: 99%

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

Anwar

Altaf

Rafique

et al. 2020

Radiomics and Radiogenomics in Neuro-Oncology

Self Cite

View full text Add to dashboard Cite

show abstract

“…T. Altaf et al, [11] presented an effective Alzheimer detection and classification algorithm. Initially, a bag of visual word methodology was utilized for improving the efficiency of texture features like LBP, scale invariant feature transform, GLCM and HOG.…”

Section: Literature Reviewmentioning

confidence: 99%

Relieff Feature Selection Based Alzheimer Disease Classification Using Hybrid Features and Support Vector Machine in Magnetic Resonance Imaging

Suresha¹,

Parthasarathi²

2019

IJCET

View full text Add to dashboard Cite

Alzheimer disease is a form of dementia that results in memory-related problems in human beings. An accurate detection and classification of Alzheimer disease and its stages plays a crucial role in human health monitoring system. In this research paper, Alzheimer disease classification was assessed by Alzheimer's disease Neuro-Imaging Initiative (ADNI) dataset. After performing histogram equalization and skull removal of the collected brain images, segmentation was carried-out using Fuzzy C-Means (FCM) for segmenting the white matter, Cerebro-Spinal Fluid (CSF), and grey matter from the pre-processed brain images. Then, hybrid feature extraction (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM)) was performed for extracting the feature values from the segmented brain images. After hybrid feature extraction, reliefF feature selection was used for selecting the optimal feature subsets or to reject the irrelevant feature vectors. Then, the selected optimal feature vectors were given as the input to a supervised classifier Support Vector Machine (SVM) to classify three Alzheimer classes of subjects; those are normal, Alzheimer disease and Mild Cognitive Impairment (MCI). The experimental outcome showed that the proposed methodology performed effectively by means of sensitivity, accuracy, specificity, and f-score. The proposed methodology enhanced the classification accuracy up to 2-20% compared to the existing methodologies.

show abstract

“…Except for ANNs and DNNs, numerous algorithms have been applied to predicted models. For example: Levenberg-Marquardt has been employed to estimate the state-of-charge of lithium-ion batteries [23]; Conjugate gradient with Powell/Beale restarts have been applied to plan the path of catering robots [24]; Polak-Ribiere conjugate gradient has been utilized to accumulate the global convergence of nonconvex functions [25]; Fletcher-Powell conjugate gradient has been used to predict component self-alignment [26]; One step secant has been applied to train a cascade ANN [27]; Resilient Backpropagation has been employed to improve the optical coherent transmission [28]; Bayesian regularization has been utilized to solve the global optimization problems [29]; Variable learning rate gradient descent has been employed to regulate the weight and threshold values of layers [30]; Support vector machine regression (Gaussian) has been utilized for heating and cooling load predictions [31]; Linear programming boosting has been employed to non-intrusive load monitoring systems [32]; Adaptive boosting has been improved to automatic wireless signal classification [33]; Extra trees classifier has been applied into natural language processing [34]; Broyden-Fletcber-Goldfarb-Shanno Quasi-Newton has been applied for brain image segmentation [35]; Moving average method has been employed to predict the solar power outputs [36]; Decision tree has been utilized to predict high-risk kidney transplantation [37]; Random subspace binary and multi-class has been applied for disease diagnosis [38]; Support vector machine regression (Linear) has been utilized to predict multi-parameter manufacturing quality [39]; Multiple proportion smoothing method has been applied to the STLF [40]; Random under sampling boosting has been employed to detect non-technical losses of electric distribution systems [41].…”

Section: Introductionmentioning

confidence: 99%

Deep Forest Regression for Short-Term Load Forecasting of Power Systems

et al. 2020

View full text Add to dashboard Cite

Deep neural networks of deep learning algorithms can be applied into regressions and classifications. While the regression performances and classification performances of the deep neural networks are depending on the hyper-parameters of the deep neural networks. To mitigate the adverse effect of the hyper-parameters for the deep learning algorithms, this paper proposes deep forest regression for the short-term load forecasting of power systems. Deep forest regression includes two procedures, i.e., multi-grained scanning procedure and cascade forest procedure. These two procedures can be effectively trained by two completely random forests and two random forests with the default configuration. Then, the deep forest regression is applied into the short-term load forecasting of power systems. The forecasting performances of deep forest regression are compared with that of numerous intelligent algorithms and conventional regression algorithms under the model with the data of previous 7-day, 21-day, and 40-day. Besides, the forecasting performances of deep forest regression with different parameters are compared. The numerical results show that the deep forest regression with default configured parameters can increase the accuracy of the short-term forecasting and mitigate the influences of the experiences for the configuration of the hyper-parameters of deep learning model. INDEX TERMS Deep forest regression, short-term load forecasting, multi-grained scanning procedure, cascade forest procedure.

show abstract

Multi-class Alzheimer's disease classification using image and clinical features

Cited by 121 publications

References 39 publications

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

Relieff Feature Selection Based Alzheimer Disease Classification Using Hybrid Features and Support Vector Machine in Magnetic Resonance Imaging

Deep Forest Regression for Short-Term Load Forecasting of Power Systems

Contact Info

Product

Resources

About