Brain stroke detection using deep learning python. Nayak DR, Padhy N, Mallick PK, Bagal DK, Kumar S.
Brain stroke detection using deep learning python 2% was attained. This research study aims to explore the current state-of-the-art deep Apr 10, 2021 · Therefore, the rapid development of deep learning has brought big prospects in the field of medicine. Implementation of the network X-NET of the paper "X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies" also in the Quaternion domain. 00 Current price is: ₹5,000. The World Health Organization (WHO) defines stroke as “rapidly developing clinical signs The improved model, which uses PCA instead of the genetic algorithm (GA) previously mentioned, achieved an accuracy of 97. [2] While all acute (or new) hemorrhages appear dense (or white) on computed tomography (CT), the primary imaging features that help Radiologists You signed in with another tab or window. 7) unique approach to detect brain strokes using machine learning techniques. Use case implementation of LSTM Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. Utilizes EEG signals and patient data for early diagnosis and intervention This project firstly aims to classify brain CT images into two classes namely 'Stroke' and 'Non-Stroke' using convolutional neural networks. Another important application of deep learning in medical images is lesion recognition. , Noguchi S. This project leverages a state-of-the-art deep learning model using DeiT (Data-Efficient Image Transformers) to predict strokes from CT scans. Mar 8, 2024 · This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. An early intervention and prediction could prevent the occurrence of stroke. 04 system. Deep Singh Bhamra Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. Deep-Learning solution for detecting Intra-Cranial Hemorrhage (ICH) 🧠 using X-Ray Scans in DICOM (. 11 Brain strokes are a major cause of disability and death globally. The proposed methodology is to Brain-Stroke-Detection-Using-Machine-Learning-for-Clinical-Decision-Support-Systems. The deep learning networks were trained and tested on a large dataset of 2,348 clinical images, and further tested on 280 images of an external dataset. 60 % accuracy. May 30, 2023 · Rapid assessment of acute ischemic stroke by computed tomography using deep convolutional neural networks. ly/3cmigiz(or)To buy this project in ONLINE, Contact:🔗Em Apr 21, 2023 · Peco602 / brain-stroke-detection-3d-cnn. This research was conducted with the help of Python and Jupyter Notebook. Jan 14, 2025 · A digital twin is a virtual model of a real-world system that updates in real-time. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. g. Machine learning algorithms are 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. Deep learning is a subfield of machine learning that aims to teach computers how to imitate human Brain Tumor Detection using Web App (Flask) that can classify if patient has brain tumor or not based on uploaded MRI image. Saleem, MA, et al. 386 - 398 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. Note: Perceptron Learning Algorithm (PLA), K-Center with Radial Basis Functions (RBF), Quadratic discriminant analysis (QDA), Linear Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. You switched accounts on another tab or window. Early detection can greatly improve patient outcomes. - hernanrazo/stroke-prediction-using-deep-learning Explore and run machine learning code with Kaggle Notebooks | Using data from National Health and Nutrition Examination Survey Stroke Prediction Using Machine Learning | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Through this study, a strategy for identifying brain stroke disease using deep learning techniques and image preprocessing is provided. 2D CNNs are commonly used to process both grayscale (1 channel) and RGB images (3 channels), while a 3D CNN represents the 3D equivalent since it takes as input a 3D volume or a sequence of 2D frames, e. 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. After the stroke, the damaged area of the brain will not operate normally. Topics IndexTerms - Brain stroke detection; image preprocessing; convolutional neural network I. Apr 27, 2023 · 6. com Mr. Code for the metrics reported in the paper is available in notebooks/Week 11 - tlewicki - metrics clean. Digit. cmpb. Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. . Oct 11, 2023 · PurposeTo develop and investigate deep learning–based detectors for brain metastases detection on non-enhanced (NE) CT. Brain Stroke Prediction Using Deep Learning: classification of brain stroke detection. For the last few decades, machine learning is used to analyze medical dataset. 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). The model's remarkable accuracy rating of 91. 6 days ago · Brain strokes are a leading reason of affliction & fatality globally, and timely diagnosis is critical for successful treatment. Earlier detection and intervention can reduce the impact of BS. In contrast, microwave imaging emerges as a promising technique, offering advantages such This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. 105711 [ DOI ] [ PubMed ] [ Google Scholar ] Nov 1, 2017 · Several studies have used common deep learning models such as Inception-V3 and EfficienNet-b0 to detect acute stroke using DW-MRI with an accuracy value of 86. 9%, according to our findings. Table of Content Few-shot Learning of CT Stroke Segmentation Based on U-Net A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework deep-learning cnn torch pytorch neural-networks classification accuracy resnet transfer-learning brain resnet-50 transferlearning cnn-classification brain About. It contains 6000 CT images. ly/3XUthAF(or)To buy this proj Jul 1, 2023 · Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. Feb 17, 2020 · Figure 1: Intracranial hemorrhage subtypes. Dec 1, 2021 · Then we applied CNN for brain tumor detection to include deep learning method in our work. DeiT Jul 30, 2024 · Early diagnosis and treatment of brain cancer depend on the detection and categorization of brain tumors. python tensorflow keras nn cnn torch quaternion atlas x-net brain-stroke-lesion-segmentation Over the past few years, stroke has been among the top ten causes of death in Taiwan. IEEE BASE PAPER ABSTRACT: Cancer is one of the foremost reasons for death worldwide, with nearly 10 million deaths noted in 2020. Sheth, “Machin e Learning in Acute Ischemic Stroke Neuroimaging, ” Frontiers in Neurology (FNEUR) 2018. The proposed methodology is to Implementation of the study: "The Use of Deep Learning to Predict Stroke Patient Mortality" by Cheon et al. Early detection using deep learning (DL) and machine Jan 1, 2024 · To achieve this goal, we have developed an early stroke detection system based on CT images of the brain coupled with a genetic algorithm and a bidirectional long short-term Memory (BiLSTM) to Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. This code is implementation for the - A. The system is Brain Stroke Detection System based on CT images using Deep Learning IEEE BASE PAPER TITLE: Innovations in Stroke Identification: A Machine Learning-Based Diagnostic Model Using Neuroimages IEEE BASE PAPER ABSTRACT: Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and 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. They experimentally verified an accuracy of more than For the last few decades, machine learning is used to analyze medical dataset. Code python database analysis pandas sqlite3 Predicting brain strokes using machine learning techniques with The context of stroke disease prediction using deep learning addressed the prevalence of imbalanced datasets with a disproportionally higher number of non-stroke cases compared to stroke cases can lead to biased models that excel at recognizing the majority class but struggle to identify individuals at risk of a stroke accurately. Deep learning systems can perform better with access to more data, which is the machine equivalent of This project describes step-by-step procedure for building a machine learning (ML) model for stroke prediction and for analysing which features are most useful for the prediction. Better methods for early detection are crucial due to the concerning increase in the number of people suffering from brain stroke. x = df. py. 2022. Imaging. Our study shows how machine learning can be used in the prediction of brain strokes by using a dataset of some common clinical features. In this project we detect and diagnose the type of hemorrhage in CT scans using Deep Learning! The training code is available in train. The system is developed using Python for the backend, with Flask serving as the web framework. Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model. 4. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. BrainStroke: A Python-based project for real-time detection and analysis of stroke symptoms using machine learning algorithms. The project utilizes a dataset of MRI images and integrates advanced ML techniques with deep learning to achieve accurate tumor detection. ipynb. Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. Anto, "Tumor detection and classification of MRI brain image using wavelet transform and SVM", 2017 International Conference on Signal Processing and Communic… Eventually, our stroke segmentation model got 0. h5 after training. ipynb contains the model experiments. 6384 IoU with 0. (2019) published "Deep Learning-Based Detection of Brain Stroke on CT Images": The authors Oct 15, 2024 · In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. Each year, according to the World Health Organization, 15 million people worldwide machine learning models stop improving after reaching a saturation threshold, deep learning models tend to perform well with large amounts of data. Arvind Choudhary Department of Computer Engineering Universal College of Engineering, Vasai, India choudharyarvind182@gmail. Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. 3% [7]. 1016/j. J. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate Jun 22, 2021 · For example, Yu et al. Nayak DR, Padhy N, Mallick PK, Bagal DK, Kumar S. This study proposes an accurate predictive model for identifying stroke risk factors. This repository contains code for a deep learning model designed to detect brain hemorrhage in MRI scans. Computer Methods and Programs in Biomedicine . Collected comprehensive medical data comprising nearly 50,000 patient records. Brain Stroke Detection System based on CT images using Deep Learning Deep Learning Python / 2024 5 JPPY2405 Oral Cancer Detection using Deep Learning Deep Learning Python / 2024 6 JPPY2406 SMS Spam Detection using Machine Learning Machine Learning Python / 2024 7 JPPY2407 Road Pothole Detection using Deep Learning Deep Learning Python / 2024 Jan 24, 2023 · Deep learning-enabled detection of acute ischemic stroke using brain computed tomography images International Journal of Advanced Computer Science and Applications , 12 ( 12 ) ( 2021 ) , pp. In the second stage, the task is making the segmentation with Unet model. It is the world’s second prevalent disease and can be fatal if it is not treated on time. , et al. Jun 24, 2022 · For this reason, stroke is considered a severe disease and has been the subject of extensive research, not only in the medical field but also in data science and machine learning studies. Brain stroke MRI pictures might be separated into normal and abnormal images Nov 19, 2024 · Welcome to the ultimate guide on Brain Stroke Prediction Using Python & Machine Learning ! In this video, we'll walk you through the entire process of making The followed approach is based on the usage of a 3D Convolutional Neural Network (CNN) in place of a standard 2D one. Upload any CT scan image, and the interface will predict whether the image shows signs of a brain stroke. drop(['stroke'], axis=1) y = df['stroke'] 12. used a CNN model in conjunction with texture analysis to detect brain strokes on CT scans. Oct 1, 2023 · To improve the detection accurateness, a technique known as fractional-order Darwinian particle swarm optimization (FODPSO) was used in the brain region that had been segmented using the expectation-maximization (EM) algorithm after the disrupted portion of the brain caused by the stroke had been identified. Therefore, the aim of BrainOK: Brain Stroke Prediction using Machine Learning Mrs. The experiment is deployed on Ubuntu16. The model has a classification accuracy of 89. Star 4. 27% uisng GA algorithm and it out perform paper result 96. Oct 1, 2022 · A CNN-based deep learning method, which can detect and classify the type of brain stroke experienced by the patient in the CT images in the dataset obtained from the Ministry of Health of the Republic of Turkey, and also find and predict the location of the stroke by segmentation, has been proposed. Brain stroke segmentation in magnetic resonance imaging (MRI) has become an evolving research area in the field of a medical imaging system. 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. Lesion Detection Network Design Aug 28, 2021 · Brain Tumour Detection Using Deep Learning | Python IEEE Final Year Project. Setting up your environment Dec 28, 2024 · Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. 2020;196 doi: 10. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. Dependencies Python (v3. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Feb 28, 2024 · Diagnosing brain tumors is a time-consuming process requiring radiologist expertise. INTRODUCTION In most countries, stroke is one of the leading causes of death. RELATED WORK Shen et al. Nov 8, 2021 · This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and In recent times, the spotlight has turned to machine learning methodologies for stroke detection due to their potential. 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. Apr 10, 2021 · Based on the collected data, automatic lesion detection is implemented using three categories of object detection networks. Topics In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. 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 A lot of papers have already been published on “brain tumor detection”. Jun 21, 2024 · Oral Cancer Detection using Deep Learning IEEE BASE PAPER TITLE: Classification of Oral Cancer Into Pre-Cancerous Stages From White Light Images Using LightGBM Algorithm. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such Jun 29, 2024 · Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. , Koyasu S. 1. 9987 specificity by using U-Net with leaky ReLU as activation function in each layer. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. Jan 20, 2023 · Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. Jun 26, 2024 · Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. 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. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. This project explores machine learning and deep learning models to classify MRI images as either stroke-positive or stroke-negative, aiming to assist medical professionals in making quicker, more accurate diagnoses. 2020. 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 in treatment planning. Scholars have explored algorithms for detecting and classifying brain tumors, focusing on precision and efficiency. About. 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 This research present the detection and segmentation of brain stroke using fuzzy c-means clustering and H2O deep learning algorithms. Our model predicts stroke with approximately 80% accuracy by using traditional Applications of deep learning in acute ischemic stroke imaging analysis. The rest of this paper is organized as follows. Furthermore, our work presents a generic method of tumor detection and extraction of its various features. dcm) format. Jun 21, 2024 · 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. ,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, 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. Since the dataset is small, the training of the entire neural network would not provide good results so the concept of Transfer Learning is used to train the model to get more accurate resul Jun 12, 2024 · This code provides the Matlab implementation that detects the brain tumor region and also classify the tumor as benign and malignant. Deep learning methodologies are being used to create automated References [1] Manish Sirsat Eduardo Ferme, Joana Camara, “Machine Learning for Brain stroke: A Review, ” Journal of stroke and cerebrovascular disease: the official journal of National Stroke Association(JSTROKECEREBROVASDIS), 20220 [2] Harish Kamal, Victor Lopez, Sunil A. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. It has been developed in a user-friendly environment using Flask via Python programming. 00 Original price was: ₹10,000. This enhancement shows the effectiveness of PCA in optimizing the feature selection process, leading to significantly better performance compared to the initial accuracy of 61. 5 ± May 23, 2024 · Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. Dec 31, 2024 · Deep learning algorithms are usually used to detection and diagnostics brain strokes Brain stroke detection and diagnostic algorithms are evaluated using performance matrices to compare and determine the best approach for predicting the onset of stroke. Reload to refresh your session. Jan 10, 2025 · Deep learning methods have shown promising results in detecting various medical conditions, including stroke. Computers. The server is equipped with NVIDIA GTX TITAN X and the CPU is Intel Xeon E5-2620 v4. If you want to view the deployed model, click on the following link: Jun 10, 2024 · Brain Stroke Detection System based on CT images using Deep Learning | Python IEEE Project 2024 - 2025. I. III. In this article, we propose a machine learning model to predict stroke diseases given patient records using Python and GridDB. The primary objective is to enhance early detection and intervention in stroke cases, leading to improved patient outcomes and potentially saving lives. Stroke Prediction Project This repository consists of files required to deploy a Machine Learning Web App created with Flask and deployed using Heroku platform. This project is an AI-powered Android application designed to detect brain strokes using advanced Deep Learning techniques. However, these modalities are associated with potential hazards or limitations. You signed out in another tab or window. Jul 28, 2020 · Machine learning techniques for brain stroke treatment. This proposed CNN model is all about increasing the accuracy while applying a transfer learning technique. 60%. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. 6765 sensitivity and 0. The model is implemented using PyTorch and trained on a custom dataset consisting of MRI images labeled with brain hemorrhage and normal classes. Most of this field's research has concentrated on applying CNN algorithms like VGG16, DNN, and ANN to this problem. Therefore, rapid detection is crucial in patients with ischemic The model is saved as stroke_detection_model. Stroke is a medical emergency in which poor blood flow to the brain causes cell death. 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. 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. 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. 🛒Buy Link: https://bit. 00. Among the several medical imaging modalities used for brain imaging Nov 21, 2024 · It provides an overview of machine learning and its applications in neuroimaging and brain stroke detection. Deep learning can effectively mine useful information from the training data and improve the accuracy and speed of medical diagnosis. JPPY2404 – Brain Stroke Detection System based on CT images using Deep Learning ₹ 10,000. The main objective of this study is to forecast the possibility of a brain stroke occurring at Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. Brain tumour classification using noble deep learning approach with parametric optimization through metaheuristics approaches. [5] as a technique for identifying brain stroke using an MRI. 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. Sep 1, 2019 · Deep learning and CNN were suggested by Gaidhani et al. With the growing patient population and increased data volume, conventional procedures have become expensive and ineffective. This repository contains the code and documentation for a project focused on the early detection of brain tumors using machine learning (ML) algorithms and convolutional neural networks (CNNs). As a result, early detection is crucial for more effective therapy. The dataset used in this project is taken from Teknofest2021-AI in Medicine competition. slices in a CT scan. However, while doctors are analyzing each brain CT image, time is running We provide a tool for detection and segmentation of ischemic acute and sub-acute strokes in brain diffusion weighted MRIs (DWIs). Medical image data is best analysed using models based on Convolutional Neural Networks (CNNs). - rchirag101/BrainTumorDetectionFlask Brain tumours pose a significant health risk, and early detection plays a crucial role in improving patient outcomes. Here, deep feature extraction was achieved using deep CNN models that had been pre-trained. Vol. Methods The study included 116 NECTs from 116 patients (81 men, age 66. strokes using texture analysis and deep learning," Gupta et al. ₹ 5,000. With just a few inputs—such as age, blood pressure, glucose levels, and lifestyle habits our advanced CNN model provides an accurate probability of stroke occurrence. The deep learning framework is PyTorch. Deep learning is a subfield of machine learning that aims to teach computers how to imitate human Sep 15, 2022 · We set x and y variables to make predictions for stroke by taking x as stroke and y as data to be predicted for stroke against x. Neha Saxena Department of Computer Engineering Universal College of Engineering, Vasai, India nehasaxena031@gmail. May 15, 2024 · When it comes to finding solutions to issues, deep learning models are pretty much everywhere. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. Augmentation techniques are applied to increase dataset diversity, such as rotating, flipping, or zooming images, enhancing model generalization. Using the Tkinter Interface: Run the interface using the provided Tkinter code. This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. Long short term memory (LSTM) 8. The Jupyter notebook notebook. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Nishio M. Deep learning algorithms have produced amazing results in medical imaging applications including tumor identification. 105711. The core of the application is a meticulously trained neural network model, which has been converted into a TensorFlow Lite format for seamless integration with the Android platform. 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. A subset of machine learning is deep learning. Mathew and P. Dec 14, 2022 · Other methods found in the literature are classification , neighbourhood-level impact based approach , Embolic Stroke Prediction , Prediction of NIH stroke scale and detection of ischemic stroke from radiology reports [26, 27] Hybrid machine learning approach scenario on genetic algorithms to improve characteristic features. 34:637–646. Vanishing and exploding gradient problem 7. When we classified the dataset with OzNet, we acquired successful performance. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. Segmenting stroke lesions accurately is a challeng-ing task, given that conventional manual techniques are time-consuming and prone to errors. Brain attack or stroke is one of the major causes of illness and death on a global level; it is important to detect it at an early stage to deal with it on time and save lives. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. vblhhsvz yokyniqc xkuc cdnd ssgn qikojw jtrhm wrhtd qckat ftrr uguwlo fhtmy czgorw fih fiwdc