Brain stroke prediction using cnn pdf 2022. 60%, and a specificity of 89.

Brain stroke prediction using cnn pdf 2022 The Optimized Deep Learning for Brain Stroke Detection approach (ODL-BSD) was put forth. Brain Stroke A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. It is one of the major causes of mortality worldwide. This study proposes an accurate predictive model for identifying stroke risk factors. They have used a decision tree algorithm for the feature selection process, a PCA Strokes damage the central nervous system and are one of the leading causes of death today. The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. 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 Kobus M. Data augmentation techniques enhance training datasets to improve classification accuracy[2]. Stroke, with the simplest definition, is a “brain attack” caused by cessation of blood flow. (2022). sakthisalem@gmail and give correct analysis. Early Brain Stroke Prediction Using Machine Learning. or ischemic stroke using a classification module, to determine whether the patient is suffering from an ischemic stroke. Despite many significant efforts and promising outcomes in this domain Oct 13, 2022 · A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach Jan 1, 2023 · Ischemic stroke is the most prevalent form of stroke, and it occurs when the blood supply to the brain tissues is decreased; other stroke is hemorrhagic, and it occurs when a vessel inside the brain ruptures. The key components of the approaches used and results obtained are that among the five Mar 4, 2022 · Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing May 15, 2024 · Brain stroke detection using deep convolutional neural network (CNN) models such as VGG16, ResNet50, and DenseNet121 is successfully accomplished by presenting a framework and fundamental principles. 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 International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www. 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 DOI: 10. Prediction of . Aug 1, 2022 · Brain tumor detection using convolution neural networks (CNN) CNN presents a segmentation-free method that eliminates the need for hand-crafted feature extractor techniques. 20–22 June 2022; Berlin/Heidelberg, Germany: Springer; 2022. using 1D CNN and batch Mar 27, 2023 · This research paper introduces a new predictive analytics model for stroke prediction using technologies of mobile health, and artificial intelligence algorithms such as stacked CNN, GMDH, and LSTM models [13,14,15,16,17,18,19,20,21,22]. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome Nov 1, 2022 · Therefore, our analysis suggests that the best possible results for stroke prediction can be achieved by using neural network with 4 important features (A, H D, A G and H T) as input. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. The ensemble Jan 31, 2025 · Early brain stroke detection using a CNN-based ResNet harnesses deep learning's power for intricate feature extraction from medical images, vital for spotting subtle stroke indications early. Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. 53%, a precision of 87. 168–180. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. This book is an accessible stroke patients relies on symptoms and injury of organs. 5 algorithm, Principal Component Jun 1, 2024 · The Algorithm leverages both the patient brain stroke dataset D and the selected stroke prediction classifiers B as inputs, allowing for the generation of stroke classification results R'. A block primarily provokes stroke in the brain’s blood supply. 850 . Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing SVM is used for real-time stroke prediction using electromyography (EMG) data. The magnetic resonance imaging (MRI) brain tumor images must be physically analyzed in this work. Oct 1, 2023 · A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. The objective of this research to develop the optimal Dec 22, 2023 · When vessels present in brain burst or the blood supply to the brain is blocked, brain stroke occurs in human body. 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. Nowadays, the physicians usually predict functional outcomes of stroke based on clinical experiences and big data, so we wish to develop a model to accurately identify imaging features for predicting functional outcomes of stroke patients. Dec 27, 2022 · Compared to several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94. A new prototype of a mobile AI health system has also been developed with high-accuracy results, which are where P k, c is the prediction or probability of k-th model in class c, where c = {S t r o k e, N o n − S t r o k e}. It is the world’s second prevalent disease and can be fatal if it is not treated on time. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Fig. Therefore, the aim of Dec 16, 2022 · PDF | The situation when the blood circulation of some areas of brain cut of is known as brain stroke. 8: Prediction of final lesion in Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to So, it is imperative to create a novel ML model that can optimize the performance of brain stroke prediction. III. The study "Deep learning-based classification and regression of interstitial Brain Strokes on CT" by H. Collection Datasets We are going to collect datasets for the prediction from the kaggle. An ML model for predicting stroke using the machine learning technique is presented in Jan 1, 2023 · Stroke is a type of cerebrovascular disorder that has a significant impact on people’s lives and well-being. Machine learning algorithms are Brain Stroke Prediction Using Deep Learning: A CNN Approach. An early intervention and prediction could prevent the occurrence of stroke. Compared with several kinds of stroke, hemorrhagic and ischemic causes have a negative impact on the human central nervous system. 65%. Jan 1, 2022 · Stroke is a medical condition that occurs when there is any blockage or bleeding of the blood vessels either interrupts or reduces the supply of blood to the brain resulting in brain cells Using CNN and deep learning models, this study seeks to diagnose brain stroke images. Stages of the proposed intelligent stroke prediction framework. Jan 1, 2021 · The fusion method has been used to improve the contrast of stroke region. Stacking. It's a medical emergency; therefore getting help as soon as possible is critical. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. 2022. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing Sep 21, 2022 · DOI: 10. Anand et al. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Nowadays, it is a very common disease and the number of patients who attack by brain stroke is skyrocketed. This paper is based on predicting the occurrenceof a brain stroke using Machine Learning. The proposed architectures were InceptionV3, Vgg-16, MobileNet, ResNet50, Xception and VGG19. , Dweik, M. 2022 international Arab conference on information technology (ACIT) 1–8 (IEEE, 2022). ResNet's residual connections aid in training deeper layers effectively, improving model performance by capturing complex spatial relationships. Digital Object Identifier 10. [7] The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" Sep 21, 2022 · DOI: 10. Aug 1, 2020 · Brain MRI is one of the medical imaging technologies widely used for brain imaging. To eectively identify brain strokes using MRI data, we proposed a deep learning-based approach. & Al-Mousa, A. We use prin- Proposed system is an automation Stroke prediction and its stages using classification techniques CNN, Densenet and VGG16 Classifier to compare the performance of these above techniques based on their execution time A. There have lots of reasons for brain stroke, for instance, unusual blood circulation across the brain. , 2021 [5] used a 3D FCNN model was used to segment gliomas and their Nov 8, 2021 · Brain tumor occurs owing to uncontrolled and rapid growth of cells. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. When the supply of blood and other nutrients to the brain is interrupted, symptoms Oct 1, 2020 · Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. 9985596 instances, including cases with Brain, using a CNN model. Jannatul Ferdous and others published An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images Jun 22, 2021 · Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals. Published on January 20, 2023. Mahesh et al. If not treated at an initial phase, it may lead to death. The suggested method uses a Convolutional neural network to classify brain stroke images into normal and pathological categories. Reddy Madhavi K. This method makes use of three improved CNN models: VGG16, DenseNet121, and ResNet50. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. In this paper, we attempt to bridge this gap by providing a systematic analysis of the various patient records for the purpose of stroke prediction. e. 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. , Jangas M. Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. This might occur due to an issue with the arteries. It's much more monumental to diagnostic the brain stroke or not for doctor, but the main Sep 1, 2023 · In recent years, deep learning-based approaches have shown great potential for brain stroke segmentation in both MRI and CT scans. In order to diagnose and treat stroke, brain CT scan images based on deep learning. 775 - 780 , 10. [11] work uses project risk variables to estimate stroke risk in older people, provide personalized precautions and lifestyle messages via web application, and use a prediction 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 1. June 2021; Sensors 21 there is a need for studies using brain waves with AI. In any of these cases, the brain becomes damaged or dies. kreddymadhavi@gmail. Professor, Department of CSE Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India. Random Forest and Decision Tree Classifications: Random Forest achieves high accuracy (~96%) in stroke prediction using structured physiological data. The majority of strokes are ischemic strokes, which happen when a blood clot obstructs or narrows an artery that supplies blood to the brain. Read Oct 11, 2023 · Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day Jan 1, 2021 · The use of deep learning, artificial intelligence, and convolutional neural network (Neethi et al. Jun 25, 2020 · K. ijres. European Journal of Electrical Engineering an d Computer Science 2023; 7(1): 23 – 30. 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. 3. A. 3169284 AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals JAEHAK YU1, SEJIN PARK 2, SOON-HYUN KWON1, KANG-HEE CHO3, AND HANSUNG Feb 4, 2025 · Acute cerebral ischemic stroke lesions are regions of brain tissue damage brought on by an abrupt cutoff of blood flow, which causes oxygen deprivation and consequent cell death. As a result, early detection is crucial for more effective therapy. In deeper detail, in [4] stroke prediction was performed on the Cardiovascular Health Study (CHS) dataset. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. 1109 stroke prediction. The leading causes of death from stroke globally will rise to 6. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate Brain Stroke Prediction Using Deep Learning: 978-1-6654-9707-7/22/$31. The study shows how CNNs can be used to diagnose strokes. The authors used Decision Tree (DT) with C4. In this research work, with the aid of machine learning (ML May 15, 2024 · This two-volume set LNCS 11383 and 11384 constitutes revised selected papers from the 4th International MICCAI Brainlesion Workshop, BrainLes 2018, as well as the International Multimodal Brain Oct 27, 2021 · Request PDF | On Oct 27, 2021, Nugroho Sinung Adi and others published Stroke Risk Prediction Model Using Machine Learning | Find, read and cite all the research you need on ResearchGate Health Organization (WHO). 3. 2%. Segmenting stroke lesions accurately is a challeng-ing task, given that conventional manual techniques are time-consuming and prone to errors. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing Nov 21, 2024 · This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. The performance of our method is tested by Mar 23, 2022 · The concern of brain stroke increases rapidly in young age groups daily. The workspreviously performed on stroke mostly include the ones on Heart stroke prediction. pp. Oct 13, 2022 · Request PDF | On Oct 13, 2022, Heena Dhiman and others published A Hybrid Model for Early Prediction of Stroke Disease | Find, read and cite all the research you need on ResearchGate Interpretable Stroke Risk Prediction Using Machine Learning Algorithms 649. AlexNet, VGG-16, VGG-19, and Residual CNN Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. patients/diseases/drugs based on common characteristics [3]. , Sobczak K. Sakthivel M Professor, Department of CSE Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India. 1Submitted on November 15, 2022. In [17], stroke prediction was made using different Artificial Intelligence methods over the Cardiovascular Health Study (CHS) dataset. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction 6 days ago · Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Stroke prediction using machine learning classification methods. we proposed certain advancements to well-known deep learning models like VGG16, ResNet50 and DenseNet121 for Apr 11, 2022 · Abstract: Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. This study presents a new machine learning method for detecting brain strokes using patient information. The accuracy of the model was 85. serious brain issues, damage and death is very common in brain strokes. We leveraged the use of the pre-trained ResNet50 model for slice classification and tissue segmentation, while we propose an efficient lightweight multi-scale CNN model (5S-CNN), which Dec 26, 2023 · Download Citation | Brain Stroke Prediction Using Deep Learning | AIoT (Artificial Intelligence of Things) and Big Data Analytics are catalyzing a healthcare revolution. One of the cerebrovascular health conditions, stroke has a significant impact on a person’s life and health. Prediction and Classification: The CNN model processes the extracted features to predict the likelihood of brain stroke. Seeking medical help right away can help prevent brain damage and other complications. Dec 1, 2023 · Stroke is a medical emergency characterized by the interruption of blood supply to the brain, resulting in the deprivation of oxygen and nutrients to brain cells [1]. 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. This deep learning method Prediction of Stroke Disease Using Deep CNN Based Approach Md. 7 million yearly if untreated and undetected by early Jan 4, 2024 · Prediction of Brain stroke using m achine learning algorithms and deep neural network techniques. 1109/ICIRCA54612. Finally, we illustrate the distribution of the accuracy values, by using the top 4 features — age, heart disease, average glucose level, hypertension from the the traditional bagging technique in predicting brain stroke with more than 96% accuracy. 1109/ACCESS. However, it is not clear which modality is superior for this task. Quantitative investigation of MRI imaging of the brain plays a critical role in analyzing and identifying therapy for stroke. The best algorithm for all classification processes is the convolutional neural network. 7. However, while doctors are analyzing each brain CT image, time is running Apr 27, 2022 · The early diagnosis of brain tumors is critical to enhancing patient survival and prospects. Early detection is crucial for effective treatment. org Volume 10 Issue 5 ǁ 2022 ǁ PP. doi: Jul 28, 2020 · 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. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. . Prediction of brain stroke using clinical attributes is prone to errors and takes Sep 25, 2024 · The goal of this is to use deep learning to detect whether there are initial signs of a brain stroke using CT or MRI images and a comparison with Vit models and attempts to discuss limitations of various architectures. May 20, 2022 · PDF | On May 20, 2022, M. After the stroke, the damaged area of the brain will not operate normally. Mar 26, 2021 · The researchers employed an RFR trained on ground truth shape, volumetric, and age variables for the overall SP. Oct 7, 2022 · Conclusion: We showed that a CNN model trained using whole-brain axial T2-weighted MR images of stroke patients would help predict upper and lower limb motor function at the chronic stage. Ashrafuzzaman1, Suman Saha2, and Kamruddin Nur3 1 Department of Computer Science and Engineering, Bangladesh University of Business 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. 57-64 For the purpose of prediction of Brain Stroke, the dataset was first acquired from Kaggle having 5110 rows and 12 columns and had attributes such as 'id', 'gender', 'age', Researchers also proposed a deep symmetric 3D convolutional neural network (DeepSym-3D-CNN) based on the symmetry property of the human brain to learn diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) difference features for automatic diagnosis of ischemic stroke disease with an AUC of 0. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. 8% with a convergence speed which is faster than that of the CNN-based unimodal Dec 1, 2022 · Stroke is one of the most serious diseases worldwide, directly or indirectly responsible for a significant number of deaths. Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. It is much higher than the prediction result of LSTM model. Discrimination Between Stroke and Brain Tumour in CT Images Based on the Texture Analysis; Proceedings of the International Conference on Information Technologies in Biomedicine; Kamień Śląski, Poland. Abstract: Brain stroke prediction is a critical task in healthcare, as early detection can significantly improve patient outcomes. , Strzelecki M. Avanija and M. , Świątek A. 00 ©2022 IEEE 776 Authorized licensed use limited to: Indian Institute of Technology Hyderabad. The situation when the blood circulation of some areas of brain cut of is known as brain stroke. Feature Extraction: Key risk factors for brain stroke are identified using Convolutional Neural Networks (CNNs), which help in extracting complex patterns and relationships between the input features. Prediction of Brain Stroke using Machine Learning as CNN, Densenet and VGG16 to evaluate the performance This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Jul 1, 2022 · A stroke is caused by a disturbance in blood flow to a specific location of the brain. , ischemic or hemorrhagic stroke [1]. Various data mining techniques are used in the healthcare industry to Jan 1, 2023 · PDF | On Jan 1, 2023, Azhar Tursynova and others published Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages | Find, read and cite all the research you need on Dec 28, 2024 · Al-Zubaidi, H. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Many predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of Jan 1, 2024 · Brain stroke prediction using deep learning: A CNN approach 2022 4th international conference on inventive research in computing applications (ICIRCA) ( 2022 ) , pp. Both of this case can be very harmful which could lead to serious injuries. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. Nov 14, 2017 · The outcomes of this research are more accurate than medical scoring systems currently in use for warning heart patients if they are likely to develop stroke. Oct 1, 2024 · Download Citation | On Oct 1, 2024, Most. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. 9. Jan 10, 2025 · In , differentiation between a sound brain, an ischemic stroke, and a hemorrhagic stroke is done by the categorization of stroke from CT scans and is facilitated by the authors using an IoT platform. It is a leading cause of mortality and long-term disability worldwide, emphasizing the need for effective diagnosis and treatment strategies. , 2022; Gautam and Raman, 2021) based methods in the diagnosis of brain diseases such as Alzheimer In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. A. Shin et al. Reddy and Karthik Kovuri and J. Many studies have proposed a stroke disease prediction model Jan 3, 2023 · The experimental results show that the proposed 1D-CNN prediction model has good prediction performance, with an accuracy of 90. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing May 23, 2024 · PDF | Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. By using a collection of brain imaging scans to train CNN models, the authors are able to accurately distinguish between hemorrhagic and ischemic strokes. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. 2. com. Deep Learning is a technique in which the system analyzes and learns, is one of the most common applications of artificial intelligence that has seen tremendous progress in the Sep 21, 2022 · DOI: 10. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. Domain Conception In this stage, the stroke prediction problem is studied, i. Anand Kumar and others published Stroke Disease Prediction based on ECG Signals using Deep Learning Techniques | Find, read and cite all the research you need on ResearchGate Jun 30, 2022 · A stroke is caused by damage to blood vessels in the brain. The majority of research has focused on the prediction of heart stroke, while just a few studies have looked at the likelihood of a brain stroke. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. The stroke is avoided in up to 80 percent of cases if the patients identify and relieve the dangers in due time. December 2022; DOI:10. Dec 15, 2023 · Download Citation | On Dec 15, 2023, Ibrahim Almubark published Brain Stroke Prediction Using Machine Learning Techniques | Find, read and cite all the research you need on ResearchGate In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. CNN achieved 100% accuracy. 60%, and a specificity of 89. With this in mind, various machine learning models are being developed to forecast the likelihood of a brain stroke. Dr. Over the past few years, stroke has been among the top ten causes of death in Taiwan. 90%, a sensitivity of 91. Brain stroke has been the subject of very few studies. Very less works have been performed on Brain stroke. Depending on the location and extent of the afflicted area, these lesions Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. In this study, Brain Stroke and other interstitial brain disorders were identified on CT images using a CNN model. In this study, we propose an ensemble learning framework for brain stroke prediction using convolutional neural networks (CNNs) and pretrained deep learning models, specifically ResNet50 and DenseNet121. In the following subsections, we explain each stage in detail. After that, a new CNN architecture has been proposed for the classification of brain stroke into two (hemorrhagic and ischemic) and three categories (hemorrhagic, ischemic and normal) from CT images. davwo oopi aaper wdir ovrfnh tsfs mpedl tpkk giv ltgal wfgm itnjlv brnk lfivnc dvcfzp

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