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Role of Deep Learning in Classification of Brain MRI Images for Prediction of Disorders: A Survey of Emerging Trends

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Abstract

Image classification is the act of labeling groups of pixels or voxels of an image based on some rules. It finds applications in medical image analysis, and satellite image identification, along with others. Numerous studies are present in the literature where the classification is done after segmentation especially in medical images to extract only necessary areas and thereby classify them based on some criteria. It finds applications in the detection of disorders and detailed study of a particular human organ of interest. In this regard, it is important to know the challenges in this field, for accurate segmentation and classification of the region of interest. Recently, deep learning (DL) based methods for the same are being used because of higher performance as compared to the handcrafted features. Increased performance comes with various challenges like complexity, the requirement of a large amount of data, and so on. This study provides a comprehensive review of issues related to recent works on segmentation and classification techniques. This review also discusses the gaps in the literature not discussed so far and put a contributed viewpoint on the same along with future directions. It will also compare and relate each work with another and examine the datasets used along with the parametric metrics and the challenges in their use. The main focus of this review is an object-based classification used in medical imagery. It is estimated that this study will address the recent challenges and provides insights into the suggestions on different types of methods being used in the current decade.

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Abbreviations

AAL:

Automated anatomical labeling

ABD:

Average boundary distance

ABIDE:

Autism brain imaging data exchange

ADHD:

Attention deficit hyperactivity disorder

ADNI:

Alzheimer’s Disease Neuroimaging Initiative

AIBL:

Australian imaging biomarkers and lifestyle study of ageing

ASD:

Autism spectrum disorder

ASSD:

Average symmetric surface distance

AUC:

Area under the curve

AVD:

Absolute volumetric difference

BraTS:

Brain tumor segmentation challenge

CSF:

Cerebrospinal fluid

CNN:

Convolutional neural networks

DARTS:

Dense U-Net-based automatic rapid tool for brain segmentation

DL:

Deep learning

DMN:

Default mode network

DSC/DC:

Dice similarity coefficient/Dice score

DTI:

Diffusion tensor imaging

EML:

Enhance mixing loss

FCN:

Fully convolutional network

GANs:

Generative adversarial networks

GMM:

Gaussian mixture model

GR:

Generalization rate

GM:

Grey matter

HD:

Hausdorff distance

HCP:

Human connectome project

HGG:

High-grade gliomas

IBSR:

Internet brain segmentation repository

ISLES:

Ischemic stroke lesion segmentation challenge

JEM:

Joint energy model

JI:

Jaccard index

LGG:

Low-grade gliomas

LSTM:

Long short-term memory

MCC:

Matthews correlation coefficient

MDD:

Major depressive disorder

MHD:

Modified hausdorff distance

MI:

Minkowski index

MRI:

Magnetic resonance imaging

MSCNNs:

Multi-slice CNNs

NCI:

National cancer institute

NPV:

Negative predictive value

PCC:

Pearson correlation coefficient

PET:

Positron emission tomography

PPMI:

Parkinson’s progression marker initiative

PPV:

Positive predictive value

PPV:

Positive predictive value

RAVD:

Relative absolute volume difference

RNN:

Recurrent neural network

ROI:

Region of interest

SACNNs:

Simple assemble CNNs

SBPCNNs:

Simple broaden plain CNNs

TCIA:

The cancer imaging archive

VD:

Volume difference

WM:

White matter

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    Poonam Rani Verma & Ashish Kumar Bhandari

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Verma, P.R., Bhandari, A.K. Role of Deep Learning in Classification of Brain MRI Images for Prediction of Disorders: A Survey of Emerging Trends. Arch Computat Methods Eng 30, 4931–4957 (2023). https://doi.org/10.1007/s11831-023-09967-0

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