Brain stroke detection using deep learning pdf. , 30 ( 7 ) ( 2021 ) , Article 105791 , 10.

Brain stroke detection using deep learning pdf 105711 [ DOI ] [ PubMed ] [ Google Scholar ] Abstract—Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. 1109/ICIRCA54612. As observed DenseNet-121 classifier provides better the detection of brain stroke. After the stroke, the damaged area of the brain will not operate normally. INTRODUCTION Stroke is a leading cause of long-term disability worldwide and represents a significant challenge for medical professionals, particularly in terms of early detection and timely intervention [1]. In their 2020 paper, "Automatic detection of brain strokes using texture analysis and deep learning," Gupta et al. Brain stroke detection is a critical medical process requiring prompt and accurate diagnosis to facilitate effective treatment. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with Fig. Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. The suggested method uses a Convolutional neural network to classify brain stroke images into normal and pathological categories. Stroke is a medical emergency in which poor blood flow to the brain causes cell death. Jan 20, 2023 · Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques January 2023 European Journal of Electrical Engineering and Computer Science 7(1):23-30 Jan 31, 2025 · In early brain stroke detection preprocessing using deep learning, standardizing and normalizing imaging data involves ensuring consistent pixel values and scaling to a standard range. used a CNN model in conjunction with texture analysis to detect brain strokes on CT scans. Healthcare providers can take proactive steps to stop the disease by identifying people who are at high risk of having a brain stroke. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. 1016/j. 2 and Jun 25, 2020 · The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. Deep Singh Bhamra BrainOK: Brain Stroke Prediction using Machine Learning Mrs. An automated early ischemic stroke detection system using CNN deep learning algorithm; Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST); Taichung, Taiwan. For the offline Jun 22, 2021 · For example, Yu et al. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. Background: Stroke is the second most leading cause of death, the World Health Organization defined stroke as 'rapidly developed clinical signs of focal (or global) disturbance of cerebral function, lasting more than 24 hours or leading to death, which is caused due to blockage or repture of brain The brain is the most complex organ in the human body. Keywords—Deep learning; machine learning; stroke; diagnosis; detection; computed tomography I. The F1 scores, precision and recall attained for the proposed model using deep learning classifiers is compared in Table 2. Segmenting stroke lesions accurately is a challeng-ing task, given that conventional manual techniques are time-consuming and prone to errors. Over the past few years, stroke has been among the top ten causes of death in Taiwan. However, while doctors are analyzing each brain CT image, time is running Nov 28, 2022 · Request PDF | Brain stroke detection from computed tomography images using deep learning algorithms | This chapter, a pre-trained CNN models that can distinguish between stroke and normal on brain Jan 10, 2025 · In , the authors demonstrated a brain stroke detection system using a deep learning model. Brain stroke segmentation in magnetic resonance imaging (MRI) has become an evolving research area in the field of a BrainOK: Brain Stroke Prediction using Machine Learning Mrs. & Camara, J Feb 4, 2025 · Prompt identification of the type of brain stroke is a pivotal measure for medical practitioners in commencing therapeutic interventions for patients afflicted with stroke. Several methods have been proposed to detect ischemic brain stroke automatically on CT scans using machine learning and deep learning, but they are not robust and their performance is not ready for clinical practice. The inability of focus in the brain due to bleeding Nov 29, 2022 · The purpose of this study is to discuss the use of convolutional neural networks, a kind of deep learning technology, in the detection of brain haemorrhage. An early intervention and prediction could prevent the occurrence of stroke. [3] Chutima Jalayondeja has conferred that in the prediction using demographic data and Decision Tree, Naïve Bayes, and Neural Network are the 3 models which were considered and Decision Tree May 23, 2024 · PDF | Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. 2020;196 doi: 10. Compared with several kinds of stroke, hemorrhagic and ischemic causes have a negative impact on the human central nervous system. cmpb. Thus, in this research work, deep learning-based brain stroke detection system is presented using improved VGGNet. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. Seeking medical help right away can help prevent brain damage and other complications. The model has a classification accuracy of 89. The F1 scores, precision and recall attained for the proposed model using deep learning classifiers is compared in Table 2 . In this paper, our purpose is to diagnose the type of stroke using high-quality images. Deep learning is a subfield of machine learning that aims to teach computers how to imitate human Nov 1, 2017 · Request PDF | On Nov 1, 2017, Chiun-Li Chin and others published An automated early ischemic stroke detection system using CNN deep learning algorithm | Find, read and cite all the research you Brain tumours pose a significant health risk, and early detection plays a crucial role in improving patient outcomes. Deep learning techniques have emerged as a promising approach for automated brain tumor detection, leveraging the power of artificial intelligence to analyse medical images accurately and efficiently. An automated early ischemic stroke detection system using CNN deep learning algorithm. Note: Perceptron Learning Algorithm (PLA), K-Center with Radial Basis Functions (RBF), Quadratic discriminant analysis (QDA), Linear Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. Jul 4, 2024 · Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Study Type 6 days ago · Brain strokes are a leading reason of affliction & fatality globally, and timely diagnosis is critical for successful treatment. Comput Med Imaging Graph 78:101673 occurs due to the interruption of blood flow to the brain[1]. -J. The purpose of this paper is to gather information or answer related to this paper’s research question Jan 1, 2021 · PDF | On Jan 1, 2021, Khalid Babutain and others published Deep Learning-enabled Detection of Acute Ischemic Stroke using Brain Computed Tomography Images | Find, read and cite all the research ischemic brain stroke automatically on CT scans using machine learning and deep learning, but they are not robust and their performance is not ready for clinical practice. The primary objective of this research was to develop a deep learning-based system for stroke detection using CT scan images and a predictive model for assessing stroke risk. Moreover, the Brain Stroke CT Image Dataset was used for stroke classification. We employ a variety of machine learning techniques, including support vector machines (SVM), decision trees, and deep learning models, to efficiently identify and categorize stroke cases from medical imaging data. Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing Sep 1, 2023 · The accurate segmentation of brain stroke lesions in medical images are critical for early diagnosis, treatment planning, and monitoring of stroke patients. 2021. , Noguchi S. Median filtering is used in the pre-processing of medical pictures. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. The system’s first component is a brain slice Jan 10, 2025 · Download Citation | On Jan 10, 2025, Tasnim Faruki and others published Detection of Brain Stroke Disease Using Deep Learning Techniques | Find, read and cite all the research you need on ResearchGate Jan 1, 2022 · Purpose To demonstrate automated detection and segmentation of brain metastases on multisequence MRI using a deep‐learning approach based on a fully convolution neural network (CNN). The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes Dec 28, 2024 · Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. Medical image data is best analysed using models based on Convolutional Neural Networks (CNNs). Jul 1, 2023 · Detection of Ischemic Brain Stroke using Deep Learning S harmila C 1 , 2 , Santhiya S 1 , Poongundran M 1 , Sanjeeth S 1 , and Pr anesh S 1 1 Computer Science and Engineering, Kongu Engi neering Nov 13, 2023 · Dataset and data processing. INTRODUCTION Deep learning is a type of machine learning that teaches computers to mimic human behaviour. Machine learning algorithms are Jul 2, 2024 · Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain’s blood flow, often caused by blood clots or artery blockages. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome Dec 1, 2023 · Alberta stroke program early CT score calculation using the deep learning-based brain hemisphere comparison algorithm J. ,26 to achieve this objective, an early stroke detection system leveraging CT brain images, alongside a genetic algorithm and a Bidirectional long short-term memory (BiLSTM) model, [1] In a research conducted by Neha Saxena, Deep Singh, Preet Maru, Arvind Choudhary they made an application of ML and Deep Learning by using ML algorithms like Logistic regression, SVM, KNN, Decision Tress and Random Forest to determine and predict the risk of Brain Stroke. Arvind Choudhary Department of Computer Engineering Universal College of Engineering, Vasai, India choudharyarvind182@gmail. Neural networks are utilized to extract complex information from medical imaging data, making the assessment of stroke indications more accurate and nuanced. Among the several medical imaging modalities used for brain imaging Jan 1, 2023 · In this chapter, deep learning models are employed for stroke classification using brain CT images. Talo M et al (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. It's a medical emergency; therefore getting help as soon as possible is critical. Reddy and Karthik Kovuri and J. The two models work as two-step deep learning models to classify brain normal, ischemic, and hemorrhagic conditions by model 01, while acute, subacute, Overall, deep learning has the potential to significantly improve the accuracy and speed of brain stroke detection, leading to better patient outcomes and ultimately saving lives. Early detection is crucial for effective treatment. Dec 1, 2020 · In recent years, deep learning algorithms have created a massive impact on addressing research challenges in different domains. The complex and ML approaches to identify brain stroke [8,22,23,24,25,26,27,28,29,30,31]. [3] survey studies on brain ischemic stroke detection using deep learning Concerning the context of brain stroke, object detection helps in the quick detection of areas of the brain affected by strokes (clots or hemorrhages), thus facilitating timely interventions. , Lin B. The organ known as the brain, which is securely protected within the skull and consists of three main parts, namely the cerebrum, cerebellum, and brainstem, is an incredibly complex and intriguing component of the human body. 7,8 For patients with suspected ischemic stroke, early detection with neuro-imaging allows for the faster exclusion of ICH and other stroke mimics, as well as rapid segmentation and prediction Apr 1, 2023 · Download Citation | On Apr 1, 2023, Naga MahaLakshmi Pulaparthi and others published Brain Stroke Detection Using DeepLearning | Find, read and cite all the research you need on ResearchGate Jun 22, 2021 · The stroke prediction module for the elderly using deep learning-based real-time EEG data proposed in this paper consists of two units, as illustrated in Figure 4 . Utilizing a pre-trained model like VGG19, transfer learning was employed to improve both accuracy and efficiency. The Stroke Detection Methods for Stroke Detection Rapid detection of time-sensitive pathologies, such as acute stroke, results in improved clinical outcomes. The presented approach incorporates an improved version of VGGNet to obtain better detection accuracy. Jul 2, 2024 · Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application July 2024 Journal of Imaging 10(7):160 International Research Journal of Modernization in Engineering Technology and Science) , 2024. jstrokecerebrovasdis. Article; E. For example, Karthik et al. Simulation analysis using a set of brain stroke data and the performance of learning algorithms are measured in terms of accuracy, sensitivity, specificity, precision, f- This research present the detection and segmentation of brain stroke using fuzzy c-means clustering and H2O deep learning algorithms. IndexTerms - Brain stroke detection; image preprocessing; convolutional neural network I. Computer Methods and Programs in Biomedicine . This paper presents a novel methodology for image reconstruction using U-Net, followed by the classification of brain stroke type Dec 31, 2024 · The contribution of this work involves is using different algorithms on a freely available dataset (from the Kaggle website), as well as methods for pre-processing the brain stroke dataset. Second, we aim to evaluate the model’s performance, focusing on accuracy and sensitivity. Nov 1, 2022 · A deep learning model based on a feed-forward multi-layer artificial neural network was also studied in [13] to predict stroke. We propose a fully automatic method for acute ischemic stroke detection on brain CT scans. Medical Imaging 2019: Computer-Aided Diagnosis, SPIE. INTRODUCTION In most countries, stroke is one of the leading causes of death. The rest of this paper is organized as follows. , 30 ( 7 ) ( 2021 ) , Article 105791 , 10. Brain stroke MRI pictures might be separated into normal and abnormal images In this study, brain stroke disease was detected from CT images by using the five most common used models in the field of image processing, one of the deep learning methods. RELEVANT WORK The majority of strokes are seen as ischemic stroke and hemorrhagic stroke and are shown in Fig. Based on them, the following sub-objectives are developed: • 1. 2020. In this study, the use of MRI and CT scans to diagnose strokes is compared. A highly non-linear scale-invariant deep brain stroke detection model, integrating networks like VGG16, network-in-network layer, and spatial pyramid pooling layer (BSD-VNS), is implemented with attributes of the SPP layer that progresses with any gauge of brain stroke measurement. First, we aim to demonstrate how Federated Learning can enhance stroke detection and prediction using Deep Learning, compared with other approaches. In recent years, deep learning-based • The main goal of this research project is to collect stroke datasets and categorise different types of strokes using machine learning and mining methods. Stroke, a condition that ranks as the second leading cause of death worldwide, necessitates immediate treatment in order to prevent any potential damage to the brain. Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST); November 2017; Taichung, Taiwan. Augmentation techniques are applied to increase dataset diversity, such as rotating, flipping, or zooming images, enhancing model generalization. Better methods for early detection are crucial due to the concerning increase in the number of people suffering from brain stroke. Recently, deep learning technology gaining success in many domain including computer vision, image recognition Overall, deep learning has the potential to significantly improve the accuracy and speed of brain stroke detection, leading to better patient outcomes and ultimately saving lives. 8–10 November 2017; pp. Brain stroke is one of the critical health issues as the after effects provides physical inability and sometimes death. Download PDF. Deep learning algorithms are usually used to detection and diagnostics brain strokes Brain stroke detection and diagnostic algorithms are evaluated using Nov 19, 2023 · As deep learning classifiers gave better accuracy in brain stroke classification as compared to machine learning classifiers, further, the performance of deep learning classifiers is evaluated. It is also known as deep structured learning and is part of a larger family of machine learning approaches based on Applications of deep learning in acute ischemic stroke imaging analysis. The Dec 31, 2021 · Deep learning techniques with VGG-16 architecture and Random Forest algorithm are implemented for detecting hemorrhagic stroke using brain CT images under segmentation. 2022. -R. This research study aims to explore the current state-of-the-art deep . KEYWORDS: Stroke detection, Computer vision, Image recognition, Deep learning, CNN 1. In Section 2, we exhibit the historical development of deep learning, including convolutional neural network (CNN), recurrent neural network (RNN), autoencoder (AE), restricted Boltzmann machine (RBM), transformer, and transfer learning (TL). -L. Aug 1, 2020 · Critical case detection from radiology reports is also studied, yet with different grounds. Jul 28, 2020 · Machine learning techniques for brain stroke treatment. 105791 Aug 1, 2022 · Meanwhile, Sercan and colleagues focus their work on brain tumour and ischemic and hemorrhagic stroke lesion studies, using deep learning capabilities through the CNN-D-UNet architecture. Medical image Mar 8, 2024 · Brain-Stroke-Detection (Using Deep Learning) This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. This project, "Brain Stroke Detection System based on CT Images using Deep Learning," leverages advanced computational techniques to enhance the accuracy and efficiency of stroke diagnosis from CT images. Similar work was explored in [14] , [15] , [16] for building an intelligent system to predict stroke from patient records. An algorithm with a seeded region growing performs classification. The program suggests using digital image processing technologies to detect infarcts and hemorrhages in human brain tissue. Nov 27, 2024 · The goals of our work are manifold. Deep Singh Bhamra *Corresponding Author: K. Since object detection enables detailed visualizations of the impact of a stroke, it becomes a valuable tool for supporting critical decisions regarding Dec 16, 2022 · This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. They experimentally verified an accuracy of more than Nov 21, 2024 · It provides an overview of machine learning and its applications in neuroimaging and brain stroke detection. The World Health Organization (WHO) defines stroke as “rapidly developing clinical signs May 15, 2024 · Download Citation | Stroke detection in the brain using MRI and deep learning models | When it comes to finding solutions to issues, deep learning models are pretty much everywhere. Mohana Sundaram 26 | Page Detection Of Brain Stroke Using Machine Learning Algorithm C) Algorithms i) Decision tree: Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. Deep learning is a subfield of machine learning that aims to teach computers how to imitate human Jul 4, 2024 · We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification Nishio M. Stroke Cerebrovasc. Early detection using artificial intelligence (AI) can significantly improve patient outcomes[3]. , Wu G. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. Deep Learning Models in Stroke Prediction: Deep learning models, particularly artificial neural networks (ANNs) and convolutional neural networks Jun 26, 2024 · Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. , Koyasu S. Neha Saxena Department of Computer Engineering Universal College of Engineering, Vasai, India nehasaxena031@gmail. One of the techniques for early stroke detection is Computerized Tomography (CT) scan. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. The proposed methodology is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. Dec 1, 2020 · The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. May 15, 2024 · When it comes to finding solutions to issues, deep learning models are pretty much everywhere. In recent times, the spotlight has turned to machine learning methodologies for stroke detection due to their potential. It is the world’s second prevalent disease and can be fatal if it is not treated on time. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement better accuracy in brain stroke classification as compared to machine learning classi-fiers, further, the performance of deep learning classifiers is evaluated. 9985596 Corpus ID: 255267780; Brain Stroke Prediction Using Deep Learning: A CNN Approach @article{Reddy2022BrainSP, title={Brain Stroke Prediction Using Deep Learning: A CNN Approach}, author={Madhavi K. To shorten the amount of time necessary to establish the massive datasets required for training the machine learning algorithms Sep 1, 2019 · Through experimental results, it is found that deep learning models not only used in non-medical images but also give accurate result on medical image diagnosis, especially in brain stroke detection. 10. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. The chapter is arranged as follows: studies in brain stroke detection are detailed in Part 2. unique approach to detect brain strokes using machine learning techniques. However, while doctors are analyzing each brain CT image, time is running Jan 4, 2024 · The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. Oct 1, 2023 · Mariano et al. , et al. For the last few decades, machine learning is used to analyze medical dataset. Dis. Saleem, MA, et al. The pre-trained ResNetl01, VGG19, EfficientNet-B0, MobileNet-V2 and GoogleNet models were run with the same dataset and same parameters. There are two types of strokes, which is ischemic and hemorrhagic. The user will get to know about the outcome of its input data. Our results imply that the deep learning-based strategy that has been described can be a useful tool for the early detection and prevention of brain stroke. deep learning for brain stroke detection-a review of recent advance Chin C. Stroke is a medical condition in which poor blood flow to the brain causes cell death and causes the brain to stop functioning properly. 368–372. This study proposes an accurate predictive model for identifying stroke risk factors. In order to diagnose and treat stroke, brain CT scan images Feb 27, 2025 · Takahashi N et al (2019) Computerized identification of early ischemic changes in acute stroke in noncontrast CT using deep learning. The Sep 1, 2019 · Deep learning and CNN were suggested by Gaidhani et al. III. The data was collected from ATLAS. proposed a pre-detection and prediction method for machine learning and deep learning-based stroke diseases that measure the electrical activities of thighs and calves with EMG biological signal sensors, which can easily be used to acquire data during daily activities. By utilizing ResNet-50's deep learning capabilities, the suggested system is able to automatically evaluate medical imaging data, including CT and MRI scans, in order to spot possible stroke symptoms. com Mr. The system uses image processing and machine learning techniques to identify and classify stroke regions within the brain, aiming to provide early diagnosis and assist medical professionals brain stroke detection is still in progress. The traditional VGGNet has more layers and the time required to train the network is high. Oct 12, 2023 · In this research work, deep learning-based brain stroke detection system is presented using improved VGGNet and Experimental results validates that the Improved VGG model attained better performance for all the parameters. D. In this study, we utilized the dataset from the Sub-Acute Ischemic Stroke Lesion Segmentation (SISS) challenge, which is a subset of the larger Ischemic Stroke Lesion The environments in which the two deep learning models were developed and implemented are detailed in Table II. The stroke is tagged, stemmed, and classified in order to accomplish the main goal. 3: Sample CT images a) ischemic stroke b) hemorrhagic stroke c) normal II. Avanija and M. One of the cerebrovascular health conditions, stroke has a significant impact on a person’s life and health. pp. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. This is achieved by discussing the state of the art approaches proposed by the have had and have not had brain strokes. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate Strokes damage the central nervous system and are one of the leading causes of death today. They used pre-processed stroke MRI for classification, trained all layers of LeNet, and distinguished between normal and abnormal patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. The proposed deep learning based brain stroke detection model is presented in this section. Two deep learning models were developed, including the 4767 CT brain images. As a result, early detection is crucial for more effective therapy. The deep learning techniques used in the chapter are described in Part 3. [5] as a technique for identifying brain stroke using an MRI. Specifically, it reviews several studies that have used techniques like random forests, artificial neural networks, support vector machines, and convolutional neural networks to accurately classify MRI scans and detect strokes with Sep 21, 2022 · DOI: 10. Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. [14] proposed a method that is both effective and quick for the creation of huge datasets for using in machine learning algorithms to the categorization of brain strokes using microwave imaging devices. Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model. A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. [Google Scholar] 12. Therefore, the aim of Dec 1, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. A preprocessing pipeline was This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Using CNN and deep learning models, this study seeks to diagnose brain stroke images. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. 9%, according to our findings. This project aims to increase the speed and accuracy of stroke diagnosis using state-of-the-art deep learning techniques, allowing for prompt medical intervention. 105711. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. krm hxecb zxdc aoxu kggj nmhv sppidz ezpsko pnfi aaxs bkntdd wupmi yqam uixhvz lhzw