Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective

Noor, Manan Binth Taj; Zenia, Nusrat Zerin; Kaiser, M. Shamim; Mahmud, Mufti; Mamun, Shamim Al

doi:10.1007/978-3-030-37078-7_12

Cited by 84 publications

(36 citation statements)

References 37 publications

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“…The recent works in the literature confirm that the feature-fusion-based methods will improve the classification accuracy without employing the complex methodologies [39][40][41]. Classification task implemented using the features of the original image and the regionof-interest (ROI) offered superior result on some image classification problems and this procedure is recommended when the similarity between the normal and the disease class images is more [24,26,31,42,43]. Hence, for the identical images, it is necessary to employ a segmentation technique to extract the ROI from the disease class image with better accuracy [26].…”

Section: Motivationmentioning

confidence: 99%

“…ML approaches are well-known for their capabilities in recognizing patterns in data. In recent years, ML has been applied to a variety of tasks including biological data mining [24,25], medical image analysis [26], financial forecasting [27], trust management [28], anomaly detection [29,30], disease detection [31,32], natural language processing [33], and strategic game playing [34].…”

Section: Introductionmentioning

confidence: 99%

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Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

et al. 2020

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The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.

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Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

et al. 2020

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“…In recent years, DL methods are potentially reshaping the future of ML and AI [ 48 ]. It is worthy to mention here that, from a broader perspective, ML has been applied to a range of tasks including anomaly detection [ 49 , 50 , 278 , 283 , 290 ], biological data mining [ 51 , 52 ], detection of coronavirus [ 53 , 54 ], disease detection and patient management [ 55 – 57 , 277 , 279 – 282 , 284 , 286 , 287 , 289 , 291 ], education [ 58 ], natural language processing [ 59 , 285 , 288 ], and price prediction [ 60 ]. Despite notable popularity and applicability to diverse disciplines [ 61 ], there exists no comprehensive review which focuses on pattern recognition in biological data and provides pointers to the various biological data sources and DL tools, and the performances of those tools [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Mining Biological Data

et al. 2021

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Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

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“…Over the last decade machine learning (ML) has been successfully applied to biological data mining [12,13], image analysis [14], financial forecasting [15], anomaly detection [16,17], disease detection [18,19], natural language processing [20,21] and strategic game playing [22]. In particular, the success of DL algorithms in computer vision, researchers of neuroimaging have also strived to use DL-based approaches for the detection of these NLD from MRI scans [3,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

et al. 2020

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Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective

Cited by 84 publications

References 37 publications

Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

Deep Learning in Mining Biological Data

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

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