Brain stroke prediction using cnn 2022 online. The objective of this research to develop the optimal .

Brain stroke prediction using cnn 2022 online Nov 1, 2022 · We provide a detailed analysis of various benchmarking algorithms in stroke prediction in this section. Jan 4, 2024 · Ashrafuzzaman M, Saha S, Nur K. The leading causes of death from stroke globally will rise to 6. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. org Volume 10 Issue 5 ǁ 2022 ǁ PP. 4 (2024): Vol 6 Issue 4 Download scientific diagram | Flow diagram of brain stroke prediction approach from publication: Brain Stroke Prediction Using Deep Learning: A CNN Approach | Deep Learning, Stroke and Brain 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}. Abstract—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 starting to die. Mar 4, 2022 · Heart disease and strokes have rapidly increased globally even at juvenile ages. [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 Aug 1, 2020 · Brain MRI is one of the medical imaging technologies widely used for brain imaging. Ischemic Oct 17, 2019 · Feng X, Tustison N, and Meyer C Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, and van Walsum T Brain tumor segmentation using an ensemble of 3D U-Nets and overall survival prediction using radiomic features Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2019 Cham Springer 279-288 Dec 15, 2022 · Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. This deep learning method Dec 22, 2023 · When vessels present in brain burst or the blood supply to the brain is blocked, brain stroke occurs in human body. The proposed model is built upon the state-of-the-art CNN architecture VGG16, employing a data augmentation approach. Stroke, with the simplest definition, is a “brain attack” caused by cessation of blood flow. 8% with a convergence speed which is faster than that of the CNN-based unimodal and give correct analysis. Oct 1, 2024 · 1 INTRODUCTION. We benchmark three popular classification approaches — neural network (NN), decision tree (DT) and random forest (RF) for the purpose of stroke prediction from patient attributes. This study proposes a machine learning approach to diagnose stroke with imbalanced 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 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]. 1109/ICIRCA54612. 4 Smoking. Jan 3, 2023 · The main goal of this paper is to propose a novel classification prediction model using an end-to-end deep neural network that avoids the process of manual feature extraction. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. Segmenting stroke lesions accurately is a challeng-ing task, given that conventional manual techniques are time-consuming and prone to errors. 9. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. The effects of smoking include increased BP and decreased oxygen levels, and high BP causes brain stroke. The proposed method was able to classify brain stroke MRI images into normal and abnormal images. Discussion. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. 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. 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. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome Apr 11, 2022 · The major cause behind stroke is disruption of blood supply due to clotting in the blood to the nerves in the brain. 3. International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www. The stroke can be major or minor. By using this system, we can predict the brain stroke earlier and take the require measures in order to decrease the effect of the stroke. According to the World Stroke Organization (WSO): Global Stroke Fact Sheet 2022, stroke remains the second leading cause of death worldwide and is one of the top three causes of disability . Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing Kobus M. It is a leading cause of mortality and long-term disability worldwide, emphasizing the need for effective diagnosis and treatment strategies. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. One of the greatest strengths of ML is its Oct 19, 2022 · Stroke is a medical condition in which the blood vessels in the brain rupture, causing brain damage. The proposed architectures were InceptionV3, Vgg-16, MobileNet, ResNet50, Xception and VGG19. Stroke prediction using machine learning classification methods. org 6 days ago · Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. Many such stroke prediction models have emerged over the recent years. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. Note: Perceptron Learning Algorithm (PLA), K-Center with Radial Basis Functions (RBF), Quadratic discriminant analysis (QDA), Linear Sep 1, 2024 · Ashrafuzzaman et al. Avanija and M. This research aims to use neural network (NN) and machine learning (ML) techniques to assess the probability of a stroke in the brain occurring . , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. M. doi: 10. Stroke can be classified into two main categories: ischemic stroke and hemorrhagic stroke . After the stroke, the damaged area of the brain will not operate normally. However, while doctors are analyzing each brain CT image, time is running 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'. Therefore, the aim of rate of population due to cause of the Brain stroke. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to the traditional bagging technique in predicting brain stroke with more than 96% accuracy. 2022 international Arab conference on information technology (ACIT) 1–8 (IEEE, 2022). When the supply of blood and other nutrients to the brain is interrupted, symptoms Jan 1, 2021 · The fusion method has been used to improve the contrast of stroke region. The severity for a stroke can be reduced by detecting it early on. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. 2022. In addition, three models for predicting the outcomes have been developed. Jan 24, 2023 · This section demonstrates the results of using CNN to classify brain strokes using different estimation parameters such as accuracy, recall accuracy, F-score, and we use a mixing matrix to show true positive, true negative, false positive, and false negative values. Over the past few years, stroke has been among the top ten causes of death in Taiwan. [28] proposed a method of diagnosing brain stroke from MRI using deep learning and CNN. This might occur due to an issue with the arteries. CNN achieved 100% accuracy. Prediction of stroke disease using deep CNN based approach. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. So, it is imperative to create a novel ML model that can optimize the performance of brain stroke prediction. 1-3 Deprivation of cells from oxygen and other nutrients during a stroke leads to the death of The situation when the blood circulation of some areas of brain cut of is known as brain stroke. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes 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. Sep 21, 2022 · 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. Oct 1, 2020 · Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. 7 million yearly if untreated and undetected by early 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. AlexNet, VGG-16, VGG-19, and Residual CNN Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. By using a collection of brain imaging scans to train CNN models, the authors are able to accurately distinguish between hemorrhagic and ischemic strokes. Many studies have proposed a stroke disease prediction model Jun 25, 2020 · K. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Stroke is an emergency health condition which has to be dealt with carefully. 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 Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke Med Biol Eng Comput . III. (CNN, LSTM, Resnet) Front Genet. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. Apr 16, 2022 · A total of 1164 NCCT brain images were collected from 62 patients with hemorrhagic stroke and used for evaluating the model. Early brain stroke prediction using machine learning AN Tusher, MS Sadik, MT Islam 2022 11th International Conference on System Modeling & Advancement in … , 2022 May 20, 2022 · PDF | On May 20, 2022, M. 9058, respectively. Both of this case can be very harmful which could lead to serious injuries. 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. 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 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', Jan 20, 2023 · Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques. Nowadays, it is a very common disease and the number of patients who attack by brain stroke is skyrocketed. , Strzelecki M. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. (2022). The average sensitivity, specificity, and accuracy of CNN prediction are 0. There have lots of reasons for brain stroke, for instance, unusual blood circulation across the brain. The proposed method takes advantage of two types of CNNs, LeNet Nov 21, 2024 · This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. 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. 20–22 June 2022; Berlin/Heidelberg, Germany: Springer; 2022. Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate 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 performance of our method is tested by Dec 27, 2022 · Compared to several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94. Ashrafuzzaman1, Suman Saha2, and Kamruddin Nur3 1 Department of Computer Science and Engineering, Bangladesh University of Business 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. 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. ijres. 2. , Świątek A. Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. Seeking medical help right away can help prevent brain damage and other complications. A. , Dweik, M. 168–180. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction based on deep learning. In recent years, some DL algorithms have approached human levels of performance in object recognition . , Ramezani, R. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing 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. 1007/s11517-022-02636-7. Reddy and Karthik Kovuri and J. Collection Datasets Jan 1, 2023 · A dataset of 13,850 MRI images of stroke patients was collected from various reliable sources, including Madras scans and labs, Radiopaedia, Kaggle datasets, and online databases. 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]. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). Sirsat et al. It's much more monumental to diagnostic the brain stroke or not for doctor, but the main This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. H, Hansen A. In this paper, we aim to detect brain strokes with the help of CT-Scan images by using a convolutional neural network. The experiments used five different classifiers, NB, SVM, RF, Adaboost, and XGBoost, and three feature selection methods for brain stroke prediction, MI, PC, and FI. May 1, 2024 · This study proposed a hybrid system for brain stroke prediction (HSBSP) using data from the Stroke Prediction Dataset. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. we proposed certain advancements to well-known deep learning models like VGG16, ResNet50 and DenseNet121 for 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. stroke patients relies on symptoms and injury of organs. References [1] Pahus S. The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. Dec 28, 2024 · Al-Zubaidi, H. 2 million new cases each year. In addition, three models for predicting the outcomes have Jan 1, 2022 · Considering the above case, in this paper, we have proposed a Convolutional Neural Network (CNN) model as a solution that predicts the probability of stroke of a patient in an early stage to Sep 21, 2022 · DOI: 10. Jul 28, 2020 · Machine learning techniques for brain stroke treatment. Oct 1, 2022 · Gaidhani et al. 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. A new prototype of a mobile AI health system has also been developed with high-accuracy results, which are 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]. Compared with 5 machine learning methods based on clinical variables, CNN can also achieve better performance. Propose a new ensemble model to predict brain strokes. T, Hvas A. , Jangas M. Stacking. Nov 19, 2023 · As per the statistics from the global stroke fact sheet 2022, stroke is the main contributor to disability and the second greatest cause of death worldwide []. This paper proposes a one-dimensional convolutional neural network (1D-CNN) classification model based on stroke EEG signal. (2020) reviewed the application of machine learning in brain stroke detection, providing a broad understanding of ML techniques in Apr 11, 2022 · The major cause behind stroke is disruption of blood supply due to clotting in the blood to the nerves in the brain. As a result, early detection is crucial for more effective therapy. Early detection is crucial for effective treatment. Student Res. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing Sep 21, 2022 · DOI: 10. 12720/jait. e. The stroke is avoided in up to 80 percent of cases if the patients identify and relieve the dangers in due time. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. pp. 13 Prediction of Stroke Disease Using Deep CNN Based Approach Md. Stroke, a leading neurological disorder worldwide, is responsible for over 12. June 2021; Sensors 21 there is a need for studies using brain waves with AI. This deep learning method Oct 13, 2022 · An accurate prediction of stroke is necessary for the early stage of treatment and overcoming the mortality rate. . It's a medical emergency; therefore getting help as soon as possible is critical. 2022 Aug 2. , et al. ResNet's residual connections aid in training deeper layers effectively, improving model performance by capturing complex spatial relationships. In minor stroke, the blood supply to some parts of the brain is hampered, and in major stroke, the person can lose life. The study shows how CNNs can be used to diagnose strokes. Shockingly, the lifetime risk of experiencing a stroke has risen by 50% in the past 17 years, with an estimated 1 in 4 individuals projected to suffer a stroke during their lifetime []. We use prin- Nov 18, 2022 · Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. 8: Prediction of final lesion in Jul 24, 2024 · Xia, H. In this research work, with the aid of machine learning (ML Dec 10, 2022 · Brain Stroke is considered as the second most common cause of death. 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. using 1D CNN and batch 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 The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. In addition, we compared the CNN used with the results of other studies. 3. Use analytics assessment metrics to validate the performance of the suggested ensemble model. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. , Sobczak K. Personalized Med. In addition, abnormal regions were identified using semantic segmentation. In [17], stroke prediction was made using different Artificial Intelligence methods over the Cardiovascular Health Study (CHS) dataset. : Prediction of stroke outcome using natural language processing-based machine learning of radiology report of brain MRI. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. 6 No. developed a Convolutional Neural Network (CNN), a technique for automation main ischemic stroke, with a view to developing and running tests authors collected 256 pictures using the CNN model. J. The workspreviously performed on stroke mostly include the ones on Heart stroke prediction. Among these images, 7,810 were identified as cases of ischemic stroke, while 6,040 represented hemorrhagic strokes. 2022 Jan 24;12:827522. 9197, 0. The best algorithm for all classification processes is the convolutional neural network. It causes the disability of multiple organs or unexpected death. 8837, and 0. Dec 31, 2024 · Although cardiac stroke prediction has received a lot of attention, brain stroke risk has received comparatively little attention. 12(1), 28 (2023) Google Scholar Heo, T. The model has been used for accurate classification of hemorrhagic stroke in NCCT brain images, which comprises normal images and ICH lesion of different sizes of ICHs. This study proposes an accurate predictive model for identifying stroke risk factors. 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 Nov 2, 2023 · To ascertain the efficacy of an automated initial ischemic stroke detection, Chin et al. Stroke is the leading cause of death and disability worldwide, according to the World Health Mar 23, 2022 · The concern of brain stroke increases rapidly in young age groups daily. It primarily occurs when the brain's blood supply is disrupted by blood clots, blocking blood flow, or when blood vessels rupture, causing bleeding and damage to brain tissue. This study presents a new machine learning method for detecting brain strokes using patient information. Nov 14, 2022 · 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. Smoking causes many health issues in the human body. The objective of this research to develop the optimal It is a condition where Stroke become damaged and cannot filter toxic wastes in the body. Mahesh et al. In order to enlarge the overall impression for their system's 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. 18 November 2022. 10(4), 286 (2020) Jun 22, 2021 · Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals. Brain stroke has been the subject of very few studies. 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. A. After 4-5 epochs, the CNN framework was well trained. They have used a decision tree algorithm for the feature selection process, a PCA Jul 1, 2023 · 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. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model 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 23, 2024 · PDF | Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. Use callbacks and reduce the learning rate depending on the validation loss. (2022) developed a stroke disease prediction model using a deep CNN-based approach, showcasing the potential of convolutional neural networks in forecasting stroke probabilities. So that it saves the lives of the patients without going to death. This paper is based on predicting the occurrenceof a brain stroke using Machine Learning. A new prototype of a mobile AI health system has also been developed with high-accuracy results, which are 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. An ML model for predicting stroke using the machine learning technique is presented in Stroke is a major cause of death and disability. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Journal of Journal of Advances in Information Technology 2022; 13(6): 604 – 613. 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. Proceedings of the SMART–2022, IEEE Conference ID: 55829 Potato and Strawberry Leaf Diseases Using CNN and Image 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. Prediction of . Very less works have been performed on Brain stroke. & Al-Mousa, A. doi: Dec 16, 2022 · Early Brain Stroke Prediction Using Machine Learning. 57-64 Using CNN and deep learning models, this study seeks to diagnose brain stroke images. After training and testing the model on a CT-scan dataset comprising 2551 images, we obtained the best accuracy of 90%. 850 . serious brain issues, damage and death is very common in brain strokes. https://doi. Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. S. M (2020), “Thrombophilia testing in 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. 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 key components of the approaches used and results obtained are that among the five Dec 29, 2022 · Cancer and stroke are interrelated because they share several risk factors that accelerate stroke mechanisms, and cancer treatments can increase the risk of stroke . This book is an accessible Most read articles by the same author(s) Rabia Tehseen, Waseeq Haider, Uzma Omer, Nosheen Qamar, Nosheen Sabahat, Rubab Javaid, Predicting Depression Among Type 2 Diabetic Patients Using Federated Learning , International Journal of Innovations in Science & Technology: Vol. Jul 1, 2022 · A stroke is caused by a disturbance in blood flow to a specific location of the brain. A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. [7] The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" 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 stroke prediction. : Analyzing the performance of TabTransformer in brain stroke prediction. Dec 1, 2024 · Develop three moderated models of Inceptionv3, MobileNetv2, and Xception using transfer learning and fine-tuning techniques. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. ewlxu okvh kozxk ttar jmibxv oty kblydli scoht xny ldhnlbzl mmw rdqypfm agafei qgkzbp ybaspdt