Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects

doi:10.1016/j.cmpb.2020.105728

Computer Methods and Programs in Biomedicine

Volume 197, December 2020, 105728

https://doi.org/10.1016/j.cmpb.2020.105728 Get rights and content

Highlights

•
This research aims to emphasize the huge impact of deep learning models in brain stroke detection and lesion segmentation.
•
The survey analyses 113 research papers published in different academic research databases in the past few years.
•
The emerging trends and breakthroughs in stroke detection have been detailed in this evaluation.
•
It concludes by examining the technical and non-technical challenges faced by researchers and the future implications in stroke detection.

Abstract

Background and objective

In recent years, deep learning algorithms have created a massive impact on addressing research challenges in different domains. The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. This is achieved by discussing the state of the art approaches proposed by the recent works in this field.

Methods

In this study, the advancements in stroke lesion detection and segmentation were focused. The survey analyses 113 research papers published in different academic research databases. The research articles have been filtered out based on specific criteria to obtain the most prominent insights related to stroke lesion detection and segmentation.

Results

The features of the stroke lesion vary based on the type of imaging modality. To develop an effective method for stroke lesion detection, the features need to be carefully extracted from the input images. This review takes an attempt to categorize and discuss the different deep architectures employed for stroke lesion detection and segmentation, based on the underlying imaging modality. This further assists in understanding the relevance of the two-deep neural network components in medical image analysis namely Convolutional Neural Network (CNN) and Fully Convolutional Network (FCN). It hints at other possible deep architectures that can be proposed for better results towards stroke lesion detection. Also, the emerging trends and breakthroughs in stroke detection have been detailed in this evaluation.

Conclusion

This work concludes by examining the technical and non-technical challenges faced by researchers and indicate the future implications in stroke detection. It could support the bio-medical researchers to propose better solutions for stroke lesion detection.

Introduction

Stroke is one of the major reasons for adult deaths around the globe, impacting 6.2 million people every year [1]. Over the past two decades, there has been a 26 percent increase in stroke deaths, worldwide. Stroke is the second leading cause of death across the globe [2]. In turn, a great amount of research has been carried out to facilitate better and accurate stroke detection. Brain stroke occurs when the blood flow to the brain is stopped or when the brain doesn't get a sufficient amount of blood. As a result, the particular part of the brain drained of blood supply experiences a shortage of oxygen and becomes unresponsive [3]. This in turn, causes disturbances to the organs that are controlled by the affected part of the brain. The primary symptom of a stroke is partial numbness and numbness in the legs, arms and face belonging to one side of the body. Other minor symptoms are dizziness, headache, difficulty in walking and unconsciousness [4].

Depending on the obstacle in the blood supply to the brain, stroke can be classified into two types, Ischemic Stroke and Hemorrhagic stroke [5]. Ischemic stroke is caused by an obstruction in the blood vessels that carry blood to the brain. This specific type accounts for almost 87% of all stroke cases [6]. Hemorrhagic stroke is majorly caused by the breakage of weak blood vessels. Aneurysms and arteriovenous malformations are the basic types of weakened blood vessels that are responsible for Hemorrhagic stroke [7]. Another cause of Hemorrhagic stroke is high blood pressure [8].

The impact of stroke in an individual is influenced by the affected region of the brain and its severity. An extreme case could potentially lead to death [9]. The challenging process concerning stroke is its treatment. If the blood flow to the brain is not restored within several hours after the onset of ischemia, then the penumbral tissue will not be salvageable. The goal of stroke treatment and therapy is to save the penumbral tissue. Once the tissue is infarcted, the process is irreversible [10].

A stroke can be caused by a ruptured blood vessel or a blocked artery [11]. Images of the brain that are recorded during a scan and physical tests are utilized in diagnosing stroke among individuals. Strokes are diagnosed using advanced imaging techniques. Diagnosis is done with the help of brain imaging procedures such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) [12]. These imaging techniques have been proven to be essential for computing the changes in the properties of the tissues. The degree of infarction of the brain tissue is characterized by these imaging techniques. Over the past few years, various medical image investigation methodologies and statistical tools have been experimented with. The performance of these tools and methodologies in differentiating amongst the states of tissues in the brain were compared.

CT scans performed on an individual presumed to have a stroke, can be used to identify the type of stroke that they have undergone. The required information for carrying out the emergency procedures right after a stroke can be provided by CT scans [13]. CT is comparatively inexpensive and less affected by noise. CT is more accessible to patients and much faster than other imaging techniques such as MRI [14], [15]. In the event of a stroke, a non-enhanced CT is the first radiological examination performed on the patient [16]. A hypodense structure in the CT images indicates the presence of an ischemic lesion [17]. However, abnormal lesions are not clearly visible in a CT. It also experiences constraints while locating minute infarcts in the cerebellum, the brain stem and the interiors of the cerebral hemispheres.

Hence, MRI is a more suitable technique for overcoming this limitation [18]. Despite the many benefits that MRI provides, MRI is expensive and accessible to only a minority of the healthcare centers. The high duration for performing an MRI scan poses serious challenges to the existing imaging techniques [19]. By using MRI instead of CT, infarcts can be detected earlier on before the appearance of symptoms.

Over the last decade, several computer-aided techniques and tools have been developed to detect abnormalities in the brain at the earliest. Specifically, Artificial Intelligence and Deep Learning are majorly used to achieve accurate and automated results for Stroke detection. These results do not play a stand-alone role in the detection process. Being a very sensitive treatment process, computer-aided techniques can be complementary to support physicians in the stroke detection process [20].

There are certain challenges involved while using computer-aided techniques to get accurate results. Firstly, high-resolution images are required. The equipment required to get these high-resolution brain images is expensive. Secondly, the diversity in brain tissues increases the difficulty in diagnosing the stroke. Another challenge is the presence of any older stroke area. This makes the distinguishing process tough between the new stroke area and the old stroke area [21]. To get an efficient and accurate computer-aided model, all these issues need to be fixed.

Through this work, we aim to achieve the following objectives

•
To compare the performance of the existing deep learning techniques used for stroke lesion detection and segmentation.
•
To discuss the achievements of the state-of-the-art techniques in stroke lesion detection.
•
To analyze the key challenges involved in stroke lesion detection and segmentation.
•
To highlight the potential research gaps and future trends related to computer-aided diagnosis of brain stroke.

Section snippets

Search strategy and organization of the review

In this study, we have referred research publication databases like Pubmed™, ScienceDirect™, IEEEXplore™, and Google Scholar™ to search the relevant publications made in the area of brain stroke detection. In addition to these sources, few significant articles were also downloaded from Springer and Wiley publications. These articles were screened in the context of deep learning methods and the search strategy process is highlighted in Fig. 1.

We applied a three level filtering process to

Neuroimaging for stroke – a walkthrough

This section presents an overview of types of stroke and its related imaging modalities employed for diagnosis and treatment.

Deep learning for stroke detection

Though Machine learning methods were successfully applied in Medical image processing for the past two decades, it suffers from few limitations. These methods relied greatly on hand crafted features designed by domain experts. As the observed data vary from patient to patient and data interpretation varies with the experience of the domain experts, it might lead to intra and inter-observer error. On the other hand, deep learning in Medical imaging has made significant progress in capturing

Discussion

A comprehensive study has been carried out in this work by examining various deep learning approaches applied to different imaging modalities. To the best of our knowledge, this is the first report, presenting the modality-wise deep learning approaches employed exclusively for stroke lesion detection.

Conclusion

Deep learning models can never replace doctors and radiological experts. But it can make a huge impact on the automation process in image processing and analysis. Computer-aided techniques for analysis of medical images have grown significantly in recent times, contributing to medical research and clinical applications. Recent progress in deep learning has shown continuous optimization in the segmentation process of stroke lesion regions from the brain. This research aimed to observe the

Declaration of Competing Interest

None.

References (113)

J. Kim et al.
Use of nanoparticle contrast agents for cell tracking with computed tomography
Bioconjug. Chem.
(2017)
P. Vilela et al.
Brain ischemia: CT and MRI techniques in acute ischemic stroke
Eur. J. Radiol.
(2017)
M. Nour et al.
Imaging of cerebral ischemia
Neurol. Clin.
(2014)
S. Koizumi et al.
Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain
Neuroscience
(2010)
S.R. Shanta et al.
Global changes in phospholipids identified by MALDI MS in rats with focal cerebral ischemia
J. Lipid Res.
(2012)
L. Chen et al.
Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks
NeuroImage
(2017)
K. Kamnitsas et al.
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Med. Image Anal.
(2017)
R. Karthik et al.
A deep supervised approach for ischemic lesion segmentation from multimodal MRI using fully convolutional network
Appl. Soft Comput.
(2019)
R. Guerrero et al.
White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks
NeuroImage
(2018)
L. Liu et al.
Efficient multi-kernel DCNN with pixel dropout for stroke MRI segmentation
Neurocomputing
(2019)

Y. Yu et al.

Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging

JAMA Netw. Open

(2020)

N. Debs et al.

Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke

Comput. Biol. Med.

(2020)

R.S. Barros et al.

Automated segmentation of subarachnoid hemorrhages with convolutional neural networks

Inf. Med. Unlocked

(2020)

Y. Xue et al.

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

NeuroImage

(2020)

P. Vlisides et al.

Perioperative stroke

Can. J. Anaesth.

(2016)

S. Kamalakannan et al.

Incidence & prevalence of stroke in India: a systematic review

Indian J. Med. Res.

(2017)

C. Hui et al.

Ischemic Stroke, StatPearls [Internet]

(2019)

K.W. Faiz et al.

Patient knowledge on stroke risk factors, symptoms and treatment options

Vascular Health and Risk Management

(2018)

E.S. Donkor

Stroke in the21stcentury: a snapshot of the burden, epidemiology, and quality of life

Stroke Res. Treat.

(2018)

M. Wajngarten et al.

Hypertension and stroke: update on treatment

Eur. Cardiol. Rev.

(2019)

F. Yang et al.

Characteristics of hemorrhagic stroke following spine and joint surgeries

Biomed. Res. Int.

(2017)

M. Salvetti et al.

Therapeutic approach to hypertensive emergencies: hemorrhagic stroke

High Blood Press. Cardiovasc. Prevent.

(2018)

Z. Chen et al.

Brain–heart interaction

Circ. Res.

(2017)

J.L. Saver

Penumbral salvage and thrombolysis outcome: a drop of brain, a week of life

Brain

(2017)

D. Ahirwar et al.

Simulation studies for non invasive classification of ischemic and hemorrhagic stroke using near infrared spectroscopy

Rowley H, Vagal A. Stroke and Stroke Mimics: Diagnosis and Treatment. 2020 Feb 15. In: Hodler J, Kubik-Huch RA, von...

S. Rudkin et al.

Imaging of acute ischemic stroke

Emerg. Radiol.

(2018)

G. Mair et al.

Imaging of acute stroke prior to treatment: current practice and evolving techniques

Br. J. Radiol.

(2014)

J. Ognard et al.

A “one-stop-shop” 4D CTA protocol using 320-row CT for advanced imaging in acute ischemic stroke: a technical note

Eur. Radiol.

(2019)

A.J. Salazar et al.

Accuracy and reliability of the recommendation for IV thrombolysis in acute ischemic stroke based on interpretation of head CT on a smartphone or a laptop

Am. J. Roentgenol.

(2020)

S. Bhattacharjee et al.

A survey on comparison analysis between EEG signal and MRI for brain stroke detection

Adv. Intell. Syst. Comput. Emerg. Technol. Data Mining Inf. Secur.

(2018)

Y. Mokli et al.

Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications

Neurol. Res. Pract.

(2019)

Y.-S. Tyan et al.

Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method

Int. J. Biomed. Imaging

(2014)

S. Ojaghihaghighi

Comparison of neurological clinical manifestation in patients with hemorrhagic and ischemic stroke

World J. Emerg. Med.

(2017)

J.F. Meschia et al.

Guidelines for the primary prevention of Stroke

Stroke

(2014)

T.D. Musuka et al.

Diagnosis and management of acute ischemic stroke: speed is critical

Can. Med. Assoc. J.

(2015)

B.R. French et al.

Acute ischemic stroke: current status and future directions

Mo. Med.

(2016)

G. Mair et al.

Effect of IV alteplase on the ischemic brain lesion at 24–48 hours after ischemic stroke

Neurology

(2018)

J.M. Biesbroek et al.

Brain infarct segmentation and registration on MRI or CT for lesion-symptom mapping

J. Vis. Exp.

(2019)

M.P. Lin et al.

Imaging of ischemic stroke

CONTINUUM

(2016)

M. Zille et al.

Neuronal death after hemorrhagic stroke in vitro and in vivo shares features of ferroptosis and necroptosis

Stroke

(2017)

S. Mirza et al.

Neuroimaging in Intracerebral Hemorrhage

Hemorrhagic Stroke

(2017)

A. Zaheer et al.

Magnetic resonance imaging of cerebral hemorrhagic stroke

Top. Magn. Reson. Imaging

(2000)

F. Macellari et al.

Neuroimaging in intracerebral hemorrhage

Stroke

(2014)

I.A. Mulder et al.

Distinguishing core from penumbra by lipid profiles using Mass Spectrometry Imaging in a transgenic mouse model of ischemic stroke

Sci. Rep.

(2019)

K.-A. Hossmann

Viability thresholds and the penumbra of focal ischemia

Ann. Neurol.

(1994)

J.A. Hankin et al.

MALDI mass spectrometric imaging of lipids in rat brain injury models

J. Am. Soc. Mass Spectrom.

(2011)

C. Janfelt et al.

Visualization by mass spectrometry of 2‐dimensional changes in rat brain lipids, including N ‐acylphosphatidylethanolamines, during neonatal brain ischemia

FASEB J.

(2012)

S.N. Whitehead et al.

Imaging mass spectrometry detection of gangliosides species in the mouse brain following transient focal cerebral ischemia and long-term recovery

PLoS ONE

(2011)

C.Y. Ho et al.

Accuracy of CT cerebral perfusion in predicting infarct in the emergency department: lesion characterization on CT perfusion based on commercially available software

Emerg. Radiol.

(2013)

Cited by (59)

Classification of brain strokes using divergence-based convolutional neural networks
2024, Biomedical Signal Processing and Control
Stroke is a cerebrovascular issue caused by reduced or interrupted blood flow to the brain, resulting in the rapid death of brain cells and potential permanent functional damage. Early detection, classification, and immediate intervention are essential to prevent severe consequences, including death or lifelong disabilities. In this study, a total of 6650 brain CT images were used, which included 1130 ischemic stroke, 1093 hemorrhagic stroke, and 4427 non-stroke cases provided by the Turkish Ministry of Health. The study first aimed to determine the presence of stroke in the brain. Secondly, in cases where stroke was detected, the type of stroke, whether ischemic or hemorrhagic, was determined. Lastly, the images were classified into three categories: non-stroke, hemorrhagic, and ischemic. A newly introduced divergence-based deep neural network (DNN) was modified and utilized for the method. Features were extracted from the convolutional neural network (CNN) using Walsh matrices, and classification was performed using the minimum distance network (MDN). Experimental results showed that when the images were binary classified as stroke vs. non-stroke and ischemic vs. hemorrhagic type, they achieved accuracies of 99.248% and 99.324%, respectively. For the three-class classification (non-stroke, ischemic, and hemorrhagic), a success rate of 99.097% was achieved. The proposed study has advantages in terms of no preprocessing stage, classification with real images without data augmentation, and the low number of parameters in the employed network. The results indicate that the proposed network successfully detects the presence and type of stroke with high accuracy and outperforms existing studies in the literature.
A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection
2024, Engineering Applications of Artificial Intelligence
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep learning methods for identifying hemorrhagic stroke on medical imaging scans has garnered significant interest in recent years. These methods are specifically designed to assist radiologists in identifying and highlighting abnormalities in medical scans. In this study, we conducted a systematic review and comprehensive gap analysis of various deep learning methodologies for automated hemorrhagic stroke identification. Our experiments were conducted using various datasets, particularly a publicly available large dataset of non-contrast computed tomography scans of the brain, in a regulated setting. Through an evaluation strategy that encompasses unexplored domain aspects, we identified the key challenges and areas for improvement. This strategy involved the utilization of multisite datasets and the evaluation based on the complexity in the appearance of hemorrhage. The subsequent analysis of the evaluation results revealed gaps in the current state of the art, emphasizing the need to bridge these gaps to achieve higher accuracy and reliability. This study offers valuable insights into the current state of automated hemorrhage stroke identification, highlighting areas for future research and development. The findings have significant implications for improving patient outcomes and alleviating the burden on radiology professionals, leading to more sustainable healthcare practices. The study concludes with recommendations for researchers in this field to foster the future development of automated tools for diagnosing hemorrhagic stroke.
A few-shot learning-based ischemic stroke segmentation system using weighted MRI fusion
2023, Image and Vision Computing
Stroke, particularly ischemic stroke, is a major cause of disability and one of the leading causes of adult mortality worldwide. Early and prompt management of stroke patients can reduce the severity of the disease. Doctors usually determine the severity of a stroke by focusing on the region of interest (ROI) in the MRI or CT scan images. An accurate and effective automatic image segmentation system can assist medical professionals as well as automatic detection and classification systems. Deep learning is the current advanced approach for dealing with machine learning and artificial intelligence. However, conventional deep learning requires a large amount of data for training, and the amount of labeled data in the medical field is limited. In this paper, we propose a few-shot learning strategy and integrate it with a base convolutional neural network model, which utilizes a self-attention mechanism to segment MRI for ischemic stroke. By combining the base model with self-attention, we can focus more on the ROI and disregard less important features. Additionally, the proposed system only selects slices with lesions and ignores unlesioned slices. This helps to improve efficiency and reduce the computational load by eliminating the need to tune unnecessary parameters. To achieve even better results, the system also combines two weighted images, FLAIR and DWI, as an early fusion process. Experiments have shown that this approach leads to higher performance compared to using the same system without fusion. The proposed system is evaluated using a publicly available dataset, ISLES 2015 SSIS, and compared with other state-of-the-art (SOTA) systems. It achieves a dice coefficient score of 0.68, which is significantly better than that of other SOTA systems.
Deep learning prediction of motor performance in stroke individuals using neuroimaging data
2023, Journal of Biomedical Informatics
The degree of motor impairment and profile of recovery after stroke are difficult to predict for each individual. Measures obtained from clinical assessments, as well as neurophysiological and neuroimaging techniques have been used as potential biomarkers of motor recovery, with limited accuracy up to date. To address this, the present study aimed to develop a deep learning model based on structural brain images obtained from stroke participants and healthy volunteers. The following inputs were used in a multi-channel 3D convolutional neural network (CNN) model: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity maps obtained from Diffusion Tensor Imaging (DTI) images, white and gray matter intensity values obtained from Magnetic Resonance Imaging, as well as demographic data (e.g., age, gender). Upper limb motor function was classified into “Poor” and “Good” categories. To assess the performance of the DL model, we compared it to more standard machine learning (ML) classifiers including k-nearest neighbor, support vector machines (SVM), Decision Trees, Random Forests, Ada Boosting, and Naïve Bayes, whereby the inputs of these classifiers were the features taken from the fully connected layer of the CNN model. The highest accuracy and area under the curve values were 0.92 and 0.92 for the 3D-CNN and 0.91 and 0.91 for the SVM, respectively. The multi-channel 3D-CNN with residual blocks and SVM supported by DL was more accurate than traditional ML methods to classify upper limb motor impairment in the stroke population. These results suggest that combining volumetric DTI maps and measures of white and gray matter integrity can improve the prediction of the degree of motor impairment after stroke. Identifying the potential of recovery early on after a stroke could promote the allocation of resources to optimize the functional independence of these individuals and their quality of life.
Automated ischemic acute infarction detection using pre-trained CNN models’ deep features
2023, Biomedical Signal Processing and Control
Cerebrovascular Diseases (CVD) constitute more than 50 % of neurological diseases requiring hospital treatment. Stroke is a type of disease that causes physical disability to survivors; approximately 85 % of strokes are ischemic. Once the symptoms have been identified, the diagnosis of the disease can be confirmed using widely available imaging techniques. This study attempts to build a model using diffusion Magnetic Resonance Imaging (MRI) to diagnose stroke.
Four different datasets were used in the study. A total of 1112 acute ischemic infarctions and 1202 normal diffusion MR images were collected for this study. In addition, two datasets had two classes, and the other dataset had four classes. These MRI datasets were used to test and compare the method. Efficient b0, DenseNet201, Resnet101, Resnet50, Inceptionresnetv2, Xception, MobileVnet2, ShuffleNet, Darknet19, NasnetLarge, AlexNet pre-trained Convolutional Neural Network (CNN) models with over 90 % classification results were used in the study. Features were obtained from the softmax/classification layers of these CNN models. The data were then classified utilizing a Support Vector Machine (SVM) classifier with 10-fold Cross-Validation (CV). The prediction vectors obtained from the classifiers were sorted and applied to the Iterative Majority Voting (IMV).
The proposed method yielded 97.93 %, 97.98 %, 99.07 %, and 96.93 % results for accuracy, F1-Score, precision, and recall for the collected dataset, respectively. For Dataset 2, 99.32 % accuracy and 100 % F1-Score, precision, and recall 100 % results were obtained. For Dataset 3, 97.96 % accuracy and 98.37 % F1-Score, 99.42 % precision, and 99.02 % recall results were obtained. For Dataset 4, accuracy, F1-Score, precision, and recall 100 % results were obtained.
In line with the results obtained with the method proposed in this study, acute ischemic infarction can be successfully classified based on diffusion MRIs. This research succeeded because it achieved high-accuracy results in four different datasets. This advanced automated system can assist neurologists in determining whether they miss anything when detecting acute ischemic infarction, thereby minimizing potential human error.
StrokeViT with AutoML for brain stroke classification
2023, Engineering Applications of Artificial Intelligence
Citation Excerpt :
Kuo et al. (2019) proposed a patch-based Fully Convolutional Neural Network (PatchFCN), which takes into account the local features in the slices but again falls short of situations where the whole stack of CT Scans can be used as input. More details of various techniques applied for classification of brain stroke are covered in the Review Paper by Karthik et al. (2020) and Sirsat et al. (2020). For the sake of completeness, we have tabled a few significant studies in Table 1.
Stroke, categorized under cardiovascular and circulatory diseases, is considered the second foremost cause of death worldwide, causing approximately 11% of deaths annually. Stroke diagnosis using a Computed Tomography (CT) scan is considered ideal for identifying whether the stroke is hemorrhagic or ischemic. However, most methods for stroke classification are based on a single slice-level prediction mechanism, meaning that the most imperative CT slice has to be manually selected by the radiologist from the original CT volume. This paper proposes an integration of Convolutional Neural Network (CNN), Vision Transformers (ViT), and AutoML to obtain slice-level predictions as well as patient-wise prediction results. While the CNN with inductive bias captures local features, the transformer captures long-range dependencies between sequences. This collaborative local-global feature extractor improves upon the slice-wise predictions of the CT volume. We propose stroke-specific feature extraction from each slice-wise prediction to obtain the patient-wise prediction using AutoML. While the slice-wise predictions helps the radiologist to verify close and corner cases, the patient-wise predictions makes the outcome clinically relevant and closer to real-world scenario. The proposed architecture has achieved an accuracy of 87% for single slice-level prediction and an accuracy of 92% for patient-wise prediction. For comparative analysis of slice-level predictions, standalone architectures of VGG-16, VGG-19, ResNet50, and ViT were considered. The proposed architecture has outperformed the standalone architectures by 9% in terms of accuracy. For patient-wise predictions, AutoML considers 13 different ML algorithms, of which 3 achieve an accuracy of more than 90%. The proposed architecture helps in reducing the manual effort by the radiologist to manually select the most imperative CT from the original CT volume and shows improvement over other standalone architectures for classification tasks. The proposed architecture can be generalized for volumetric scans aiding in the patient diagnosis of head and neck, lungs, diseases of hepatobiliary tract, genitourinary diseases, women’s imaging including breast cancer and various musculoskeletal diseases. Code for proposed stroke-specific feature extraction with the pre-trained weights of the trained model is available at: https://github.com/rishiraj-cs/StrokeViT_With_AutoML.

View all citing articles on Scopus

View full text

Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects

Highlights

Abstract

Background and objective

Methods

Results

Conclusion

Introduction

Section snippets

Search strategy and organization of the review

Neuroimaging for stroke – a walkthrough

Deep learning for stroke detection

Discussion

Conclusion

Declaration of Competing Interest

Bioconjug. Chem.

Eur. J. Radiol.

Neurol. Clin.

Neuroscience

J. Lipid Res.

NeuroImage

Med. Image Anal.

Appl. Soft Comput.

NeuroImage

Neurocomputing

JAMA Netw. Open

Comput. Biol. Med.

Inf. Med. Unlocked

NeuroImage

Perioperative stroke

Can. J. Anaesth.

Incidence & prevalence of stroke in India: a systematic review

Indian J. Med. Res.

Ischemic Stroke, StatPearls [Internet]

Patient knowledge on stroke risk factors, symptoms and treatment options

Vascular Health and Risk Management

Stroke in the21stcentury: a snapshot of the burden, epidemiology, and quality of life

Stroke Res. Treat.

Hypertension and stroke: update on treatment

Eur. Cardiol. Rev.

Characteristics of hemorrhagic stroke following spine and joint surgeries

Biomed. Res. Int.

Therapeutic approach to hypertensive emergencies: hemorrhagic stroke

High Blood Press. Cardiovasc. Prevent.

Brain–heart interaction

Circ. Res.

Penumbral salvage and thrombolysis outcome: a drop of brain, a week of life

Brain

Simulation studies for non invasive classification of ischemic and hemorrhagic stroke using near infrared spectroscopy

Imaging of acute ischemic stroke

Emerg. Radiol.

Imaging of acute stroke prior to treatment: current practice and evolving techniques

Br. J. Radiol.

A “one-stop-shop” 4D CTA protocol using 320-row CT for advanced imaging in acute ischemic stroke: a technical note

Eur. Radiol.

Accuracy and reliability of the recommendation for IV thrombolysis in acute ischemic stroke based on interpretation of head CT on a smartphone or a laptop

Am. J. Roentgenol.

A survey on comparison analysis between EEG signal and MRI for brain stroke detection

Adv. Intell. Syst. Comput. Emerg. Technol. Data Mining Inf. Secur.

Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications

Neurol. Res. Pract.

Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method

Int. J. Biomed. Imaging

Comparison of neurological clinical manifestation in patients with hemorrhagic and ischemic stroke

World J. Emerg. Med.

Guidelines for the primary prevention of Stroke

Stroke

Diagnosis and management of acute ischemic stroke: speed is critical

Can. Med. Assoc. J.

Acute ischemic stroke: current status and future directions

Mo. Med.

Effect of IV alteplase on the ischemic brain lesion at 24–48 hours after ischemic stroke

Neurology

Brain infarct segmentation and registration on MRI or CT for lesion-symptom mapping

J. Vis. Exp.

Imaging of ischemic stroke

CONTINUUM

Neuronal death after hemorrhagic stroke in vitro and in vivo shares features of ferroptosis and necroptosis

Stroke

Neuroimaging in Intracerebral Hemorrhage