Brain stroke prediction using cnn 2021 python Seeking medical help right away can help prevent brain damage and other complications. Brain stroke has been the subject of very few studies. Padmavathi,P. Sort options. proposed CNN-based DenseNet for stroke disease classification and prediction based on ECG data collected using 12 leads, and they obtained 99. However, they used other biological signals that are not Jun 9, 2021 · An automatic detection of ischemic stroke using CNN Deep learning algorithm. Medical imaging plays a vital role in discovering and examining the precise performance of organs The performance of object detection has increased dramatically by taking advantage of recent advances in deep learning. Bayesian Rule Lists are proposed to generate rules to predict stroke risk using decision lists. It takes different values such as Glucose, Age, Gender, BMI etc values as input and predict whether the person has risk of stroke or not. 7) would have a major risk factors of a Brain Stroke. Through this study, a strategy for identifying brain stroke disease using deep learning techniques and image preprocessing is provided. Sep 21, 2022 · Towards effective classification of brain hemorrhagic and ischemic stroke using CNN In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. It showed more than 90% accuracy. Several risk factors believe to be related to Jan 1, 2021 · The fusion method has been used to improve the contrast of stroke region. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. 2021. Deep learning is capable of constructing a nonlinear For the last few decades, machine learning is used to analyze medical dataset. The best algorithm for all classification processes is the convolutional neural network. Star 4. Nov 8, 2021 · Brain tumor and stroke lesions. Python 3. 9. [9] “Effective Analysis and Predictive Model of Stroke Disease using Classification Methods”-A. Contribute to kishorgs/Brain-Stroke-Detection-Using-CNN development by creating an account on GitHub. Sep 1, 2019 · Deep learning and CNN were suggested by Gaidhani et al. 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. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. 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. The leading causes of death from stroke globally will rise to 6. June 2021; Sensors 21 there is a need for studies using brain waves with AI. stroke prediction. Bosubabu,S. Nov 21, 2024 · This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. Jan 1, 2023 · Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages Gautam et al. 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. Therefore, the aim of Jun 12, 2024 · This code provides the Matlab implementation that detects the brain tumor region and also classify the tumor as benign and malignant. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model Oct 27, 2020 · The brain is an energy-consuming organ that heavily relies on the heart for energy supply. 99% training accuracy and 85. To address challenges in diagnosing brain tumours and predicting the likelihood of strokes, this work developed a machine learning-based automated system that can uniquely identify, detect, and classify brain tumours and predict the occurrence of strokes using relevant features. Given the rising prevalence of strokes, it is critical to understand the many factors that contribute to these occurrences. It is the world’s second prevalent disease and can be fatal if it is not treated on time. When the supply of blood and other nutrients to the brain is interrupted, symptoms 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). 07, no. The performance of our method is tested by About. Very less works have been performed on Brain stroke. It's a medical emergency; therefore getting help as soon as possible is critical. Reddy and Karthik Kovuri and J. CNN have been shown to have excellent performance in automating multiple image classification and detection tasks. III. March 2022 as Python or R do. [8] “Focus on stroke: Predicting and preventing stroke” Michael Regnier- This paper focuses on cutting-edge prevention of stroke. Potato and Strawberry Leaf Diseases Using CNN and Image ICCCNT51525. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. The Brain stroke prediction model is trained on a public dataset provided by the Kaggle . , 2022; Gautam and Raman, 2021) based methods in the diagnosis of brain diseases such as Alzheimer Explore and run machine learning code with Kaggle Notebooks | Using data from National Health and Nutrition Examination Survey Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. C, 2021 Predicting Brain Stroke using Machine Learning algorithms Topic Using a machine learning algorithm to predict whether an individual is at high risk for a stroke, based on factors such as age, BMI, and occupation. I. proposed SwinBTS, a new 3D medical picture segmentation approach, which combines a transformer, CNN, and encoder-decoder structure to define the 3D brain tumor semantic segmentation job and achieves excellent segmentation results on the public multimodal brain Tumor datasets of 2019-2021 (include T1,T1-ce,T2,T2-Flair) . The suggested method uses a Convolutional neural network to classify brain stroke images into normal and pathological categories. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. A novel Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. The key components of the approaches used and results obtained are that among the five different classification algorithms used Naïve BRAIN STROKE PREDICTION BY USING MACHINE LEARNING S. Stroke Risk Prediction Using Machine Learning Algorithms Rishabh Gurjar 1 , Sahana H K 1 , Neelambika C 1 , Sparsha B Sathish 1 , Ramys S 2 1 Department of Computer Science and Engineering. Jun 22, 2021 · Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals. Dependencies Python (v3. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome 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. . To implement a brain stroke system using SVM (Support Vector Machine) and ML algorithms (Random Forest, Decision tree, Logistic Regression, KNN) for more accurate result. One of the greatest strengths of ML is its Apr 21, 2023 · Peco602 / brain-stroke-detection-3d-cnn. Avanija and M. Deep learning in Python uses a CNN model to categorize brain MRI images for Alzheimer's stages. Sort: Most stars. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. Mathew and P. Created a Web Application using Streamlit and Machine learning models on Stroke prediciton Whether the paitent gets a stroke or not on the basis of the feature columns given in the dataset This Streamlit web app built on the Stroke Prediction dataset from Kaggle aims to provide a user-friendly Dec 10, 2022 · Brain Stroke is considered as the second most common cause of death. In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. Sudha, Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. This code is implementation for the - A. 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. Using CNN and deep learning models, this study seeks to diagnose brain stroke images. gender False age False hypertension False heart_disease False ever_married False work_type False residence_type False avg_glucose_level False bmi True smoking_status False stroke False dtype: bool There are 201 missing values in the bmi column <class 'pandas. After the stroke, the damaged area of the brain will not operate normally. Complex & Intelligent Systems. Apr 10, 2021 · Faster R-CNN may use VGG-16 or ResNet-101 for feature extraction. Apr 27, 2023 · The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. It standardizes the brain stroke dataset and evaluates the performance of different classifiers. 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. 957 ACC. INTRODUCTION 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. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction 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. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. Vasavi,M. The proposed methodology is to Contribute to Chando0185/Brain_Stroke_Prediction development by creating an account on GitHub. Seeking medical help right away 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. Decision Tree, Bayesian Classifier, Neural Networks 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. International Journal of Telecommunications. Despite 96% accuracy, risk of overfitting persists with the large dataset. In addition, three models for predicting the outcomes have Jan 20, 2023 · Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. Sep 21, 2022 · DOI: 10. Visualization : Includes model performance metrics such as accuracy, ROC curve, PR curve, and confusion matrix. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. published in the 2021 issue of Journal of Medical Systems. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. Khade, "Brain Stroke Prediction Portal Using Machine Learning," vol. Some prediction models have been devel-oped for patients with preexisting cardiovascular conditions while also being accurate for patients without the condition [7]. - Akshit1406/Brain-Stroke-Prediction 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 key components of the approaches used and results obtained are that among the five different classification algorithms used Naïve Bayes Apr 22, 2023 · Stroke is a health ailment where the brain plasma blood vessel is ruptured, triggering impairment to the brain. This might occur due to an issue with the arteries. 2022. The features in multiple dimensions and states were calculated through in-depth mining of features in the whole brain, and the prediction accuracy was improved. Early intervention and preventive measures can be taken to reduce the likelihood of stroke occurrence, potentially saving lives and improving the quality of life for patients. Stroke is the leading cause of bereavement and disability Dec 28, 2024 · Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. Aswini,P. 4 Bias field correction a input, b estimated, c ones on Heart stroke prediction. Nov 1, 2022 · In our experiment, another deep learning approach, the convolutional neural network (CNN) is implemented for the prediction of stroke. An early intervention and prediction could prevent the occurrence of stroke. , 2016), the complex factors at play (Tazin et al. 3. If the user is at risk for a brain stroke, the model will predict the outcome based on that risk, and vice versa if they do not. The effectiveness of several machine learning (ML Developed using libraries of Python and Decision Tree Algorithm of Machine learning. Machine learning algorithms are Apr 10, 2024 · All 11 Jupyter Notebook 5 Python 5 MATLAB 1. frame. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. python database analysis pandas sqlite3 brain-stroke. The random forest classifier provided the highest accuracy among the models for detecting brain stroke. A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. This paper is based on predicting the occurrence of a brain stroke using Machine Learning. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). Detection of the stroke . , 2021, [50] P_CNN_WP 2D CT 92% 92%--Gautam et T o demonstrate the model, a w eb application w for stroke, such as age and genetic predisposition [5]. Medical input remains crucial for accurate diagnosis, emphasizing the need for extensive data collection. Nov 22, 2024 · Stroke is a serious medical condition that can result in death as it causes a sudden loss of blood supply to large portions of brain. Over the past few years, stroke has been among the top ten causes of death in Taiwan. In addition, we compared the CNN used with the results of other studies. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. No Stroke Risk Diagnosed: The user will learn about the results of the web application's input data through our web application. using 1D CNN and batch Jul 24, 2024 · The aim of the study is to develop a reliable and efficient brain stroke prediction system capable of accurately predicting brain stroke. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. 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. Jupyter Notebook is used as our main computing platform to execute Python cells. These factors have been used to propose multiple stroke prediction models [6]. GridDB. User Interface : Tkinter-based GUI for easy image uploading and prediction. The project utilizes a dataset of MRI images and integrates advanced ML techniques with deep learning to achieve accurate tumor detection. [5] as a technique for identifying brain stroke using an MRI. Brain stroke MRI pictures might be separated into normal and abnormal images stroke mostly include the ones on Heart stroke prediction. , 2021, Khan Mamun and Elfouly, 2023, Lella et al. 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}. BrainStroke: A Python-based project for real-time detection and analysis of stroke symptoms using machine learning algorithms. The authors examine research that predict stroke risk variables and outcomes using a variety of machine learning algorithms, like random forests, decision trees also neural networks. Code Brain stroke prediction using machine learning. Therefore, four object detection networks are experimented overall. Jan 14, 2025 · A digital twin is a virtual model of a real-world system that updates in real-time. May 3, 2024 · Based on the above, this study proposed a stroke outcome prediction method based on the combined strategy of dynamic and static features extracted from the whole brain. 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 A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Dec 6, 2024 · In this work, brain tumour detection and stroke prediction are studied by applying techniques of machine learning. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. SaiRohit Abstract A stroke is a medical condition in which poor blood flow to the brain results in cell death. Collection Datasets We are going to collect datasets for the prediction from the kaggle. In recent years, some DL algorithms have approached human levels of performance in object recognition . a stroke clustering and prediction system called Stroke MD. 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. 123. May 23, 2024 · For this purpose, numerus widely known pretrained convolutional neural networks (CNNs) such as GoogleNet, AlexNet, VGG-16, VGG-19, and Residual CNN were used to classify brain stroke CT images as 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. 03, p. 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. The basic requirements you will need is basic knowledge on Html, CSS, Python and Functions in python. Segmenting stroke lesions accurately is a challeng-ing task, given that conventional manual techniques are time-consuming and prone to errors. 3. Future Direction: Incorporate additional types of data, such as patient medical history, genetic information, and clinical reports, to enhance the predictive accuracy and reliability of the model. Effective Analysis and Predictive Model of Stroke Disease using Classification Methods. We use prin- Jul 17, 2023 · English | 2021 | ISBN: 979-8473532579 | 358 Pages | EPUB | 19 MB. Jan 1, 2021 · The use of deep learning, artificial intelligence, and convolutional neural network (Neethi et al. Jiang et al. com. Dec 1, 2021 · This document summarizes different methods for predicting stroke risk using a patient's historical medical information. 82% testing accuracy using fine-tuned models for the correlation between stroke and ECG. core. In [17], stroke prediction was made using different Artificial Intelligence methods over the Cardiovascular Health Study (CHS) dataset. They have used a decision tree algorithm for the feature selection process, a PCA Dec 16, 2022 · Early Brain Stroke Prediction Using Machine Learning. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. This is our final year research based project using machine learning algorithms . As a result, early detection is crucial for more effective therapy. By implementing a structured roadmap, addressing challenges, and continually refining our approach, we achieved promising results that could aid in early stroke detection. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Analyze the non-contrast computed tomography with the deep learning model to be created, classify it for the presence or absence of stroke, classify the type of the stroke (Hemorrhagic or Ischemic), and pixel-wise segmentation of the stroke region in the tomography image. 9579940. Early detection using deep learning (DL) and machine calculated. the traditional bagging technique in predicting brain stroke with more than 96% accuracy. Image pre-processing computer aided detection, Data augmentation, Convolutional Neural Network. Using EHR data for stroke prediction by DNN in This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing Jun 22, 2021 · In another study, Xie et al. We use GridDB as our main database that stores the data used in the machine learning model. 2. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. 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'. The main objective of this study is to forecast the possibility of a brain stroke occurring at an Jan 10, 2025 · Deep learning methods have shown promising results in detecting various medical conditions, including stroke. 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 Considering the above stated problems, this paper presents an automatic stroke detection system using Convolutional Neural Network (CNN). Stacking. 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 Machine Learning Model: CNN model built using TensorFlow for classifying brain stroke based on CT scan images. In our configuration, the number of hidden layers is four while the first two layers are convolutional layers and the last two layers are linear layers, the hyperparameters of the CNN model is given in Table 4 . This dataset comprises 4,981 records, with a distribution of 58% females and 42% males, covering age ranges from 8 months to 82 years. Discussion. The objective of this research to develop the optimal Feb 11, 2022 · In this article you will learn how to build a stroke prediction web app using python and flask. Keywords - Machine learning, Brain Stroke. and A. Brain stroke prediction dataset. Sep 21, 2022 · A CT scan (computed tomography) image dataset is used to predict and classify strokes to create a deep learning application that identifies brain strokes using a convolution neural network. 2021; Python Apr 16, 2024 · The development and use of an ensemble machine learning-based stroke prediction system, performance optimization through the use of ensemble machine learning algorithms, performance assessment This project provides a comprehensive comparison between SVM and CNN models for brain stroke detection, highlighting the strengths of CNN in handling complex image data. 7, 2021. Dec 5, 2021 · Over the recent years, a multitude of ML methodologies have been applied to stroke for various purposes, including diagnosis of stroke (12, 13), prediction of stroke symptom onset (14, 15), assessment of stroke severity (16, 17), characterization of clot composition , analysis of cerebral edema , prediction of hematoma expansion , and outcome Sep 1, 2024 · Although progress in the implementation of modern imaging and diagnostic technology may help in diagnosis and accurate stroke prediction (Chantamit-O-Pas and Goyal, 2017, Jeon et al. Fig. 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. The entire process will be implemented with Python GUI for a user-friendly experience. , 2019, Meier et al. It is now a day a leading cause of death all over the world. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. In this thorough analysis, the use of machine learning methods for stroke prediction is covered. Ischemic Stroke, transient ischemic attack. Symptoms may appear when the brain's blood flow and other nutrients are disrupted. First, the Region Proposal Network (RPN) is used to generate the Region of Interest (ROI), and then the generated ROI is classified and regressed. The Faster R-CNN algorithm uses a two-stage detection architecture. [91] 2021 CNN model FLAIR, (T1T1C, and T2) weighted. Many such stroke prediction models have emerged over the recent years. drop(['stroke'], axis=1) y = df['stroke'] 12. 0. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing 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. x = df. We use Python thanks Anaconda Navigator that allow deploying isolated working environments. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. A brain tumor is an intracranial mass consisting of irregular growth of brain tissue cells. A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Anto, "Tumor detection and classification of MRI brain image using wavelet transform and SVM", 2017 International Conference on Signal Processing and Communic… 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. 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 The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. ResNet's residual connections aid in training deeper layers effectively, improving model performance by capturing complex spatial relationships. Using the publicly accessible stroke prediction dataset, the study measured four commonly used machine learning methods for predicting brain stroke recurrence, which are as follows: (i) Random forest (ii) Decision tree (iii) The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. Utilizes EEG signals and patient data for early diagnosis and intervention Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. DataFrame'> Int64Index: 4909 entries, 9046 to 44679 Data columns (total 11 columns): # Column Non-Null Count Dtype Jul 1, 2022 · A stroke is caused by a disturbance in blood flow to a specific location of the brain. , 2017, M and M. Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. 1109/ICIRCA54612. , 2021, Cho et al. Jun 24, 2022 · We are using Windows 10 as our main operating system. Most stars Fewest A Brain-Age Prediction Case Study" - BIBM 2023. A strong prediction framework must be developed to identify a person's risk for stroke. The Nov 26, 2021 · The most common disease identified in the medical field is stroke, which is on the rise year after year. Mar 4, 2022 · Optimizing Predictions of Brain Stroke Using Machine Learning. "No Stroke Risk Diagnosed" will be the result for "No Stroke".
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