Brain stroke prediction using cnn 2021 free. 53%, a precision of 87.

Brain stroke prediction using cnn 2021 free When brain cells don’t get enough oxygen and International Journal of Telecommunications. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. 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 []. Stroke, a leading neurological disorder worldwide, is responsible for over 12. Google Scholar; 23 ; Gurjar R, Sahana K, Sathish BS. The suggested method uses a Convolutional neural network to classify brain stroke images into normal and pathological categories. Nov 26, 2021 · The most common disease identified in the medical field is stroke, which is on the rise year after year. Journal of Journal of Advances in Information Technology 2022; 13(6): 604 – 613. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. Khade, "Brain Stroke Prediction Portal Using Machine Learning," vol. J Healthc Eng 26:2021. The Sep 21, 2022 · DOI: 10. It's a medical emergency; therefore getting help as soon as possible is critical. Mar 23, 2022 · Join for free. 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. The quantitative analysis of brain MRI images plays an important role in the diagnosis and treatment of 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 Mar 1, 2023 · The stroke-specific features are as simple as initial slice prediction, the total number of predictions, and longest sequence of prediction for hemorrhage, infarct, and normal classes. [91] 2021 CNN model FLAIR, (T1T1C, and T2) weighted. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke Jun 22, 2021 · In another study, Xie et al. Both of this case can be very harmful which could lead to serious injuries. Jannatul Ferdous and others published An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images 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. Discussion. According to the World Health Organization (WHO), stroke is the greatest cause of death a … Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. [2] presented a series of 2D and 3D models for segmenting gliomas from MRI of the brain and predicting the overall survival (OS) time of a stroke clustering and prediction system called Stroke MD. NeuroImage Clin. According to the WHO, stroke is the 2nd leading cause of death worldwide. Keywords - Machine learning, Brain Stroke. , 2021, Khan Mamun and Elfouly, 2023, Lella et al. However, they used other biological signals that are not Apr 27, 2022 · The early diagnosis of brain tumors is critical to enhancing patient survival and prospects. 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. When the supply of blood and other nutrients to the brain is interrupted, symptoms Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. Mar 4, 2022 · A. 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. 7, 2021. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing 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. Ashrafuzzaman 1, Suman Saha 2, and Kamruddin N ur 3. July 2021 · International make them easy to borrow Stroke is a disease that affects the arteries leading to and within the brain. Sudha, Mar 1, 2024 · Early stroke disease prediction with facial features using convolutional neural network model March 2024 IAES International Journal of Artificial Intelligence (IJ-AI) 13(1):933 The brain is the most complex organ in the human body. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. 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. The main objective of this study is to forecast the possibility of a brain stroke occurring at an This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. Jan 1, 2025 · In the initial phase, the Magnetic Resonance Imaging (MRI) brain images are acquired from the Brain Tumor Image Segmentation Challenge (BRATS) 2019, 2020 and 2021 databases. 07, no. , 2021, [50] P_CNN_WP 2D Prediction of motor outcome of stroke patients using a deep learning algorithm with brain MRI as input data. 65%. Public Full-text 1 Prediction of Stroke Disease Using Deep CNN . Jan 1, 2021 · This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average Dec 1, 2021 · This document summarizes different methods for predicting stroke risk using a patient's historical medical information. After the stroke, the damaged area of the brain will not operate normally. Stacking. Jiang, D. doi: 10. Kshirsagar, H. Based Approach . This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Join for free. 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. Using CT or MRI scan pictures, a classifier can predict brain stroke. Google Scholar Gaidhani BR, Rajamenakshi RR, Sonavane S (2019) Brain stroke detection using convolutional neural network and deep learning models. 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 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'. Apr 16, 2024 · The development of a stroke prediction system using Random Forest machine learning algorithm is the main objective of this thesis. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Brain stroke prediction dataset. Available via license: (CNN, LSTM, Resnet) Jan 1, 2022 · Join for free. Jul 1, 2022 · Join for free. The Brain stroke prediction model is trained on a public dataset provided by the Kaggle . Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Bayesian Rule Lists are proposed to generate rules to predict stroke risk using decision lists. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. Jan 3, 2023 · The experimental results show that the proposed 1D-CNN prediction model has good prediction performance, with an accuracy of 90. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. Prediction of stroke thrombolysis outcome using CT brain machine learning. June 2021; Sensors 21 there is a need for studies using brain waves with AI. 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. Avanija and M. Prediction of stroke disease using deep CNN based approach. 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. com. Cai, and X. This dataset comprises 4,981 records, with a distribution of 58% females and 42% males, covering age ranges from 8 months to 82 years. (2022) used 3D CNN for brain stroke classification at patient level. Loya, and A. 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. Chin et al published a paper on automated stroke detection using CNN [5]. 03, p. Read Apr 10, 2021 · Stroke is a kind of cerebrovascular disease that heavily damages people’s life and health. Nov 8, 2021 · Join for free. 10. In this research work, with the aid of machine learning (ML Dec 22, 2023 · When vessels present in brain burst or the blood supply to the brain is blocked, brain stroke occurs in human body. International Journal of Advanced Computer Science And Applications. , 2019, Meier et al. 2022. 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. In 2017, C. L. The leading causes of death from stroke globally will rise to 6. 85 (6), 460–466. Sheetal, Prakash Choudhary, Thongam Khelchandra. Jul 1, 2023 · Sailasya G and Kumari G. This book is an accessible 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 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 or ischemic stroke using a classification module, to determine whether the patient is suffering from an ischemic stroke. Jan 1, 2023 · Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages ratio of the n umber of accurate predictions to the total n umber of Gautam et al. There are a couple of studies that have performed stroke classification on 3D volume using 3D CNN. The ensemble Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. The proposed methodology is to classify brain stroke MRI images into normal and abnormal images and delineate abnormal regions using semantic segmentation [4]. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. It is the world’s second prevalent disease and can be fatal if it is not treated on time. Public Full-text 1. efficient than typical systems which are currently in use for treating stroke diseases. serious brain issues, damage and death is very common in brain strokes. , 2016), the complex factors at play (Tazin et al. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. 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 Mar 27, 2023 · 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 code is implementation for the - A. In this thorough analysis, the use of machine learning methods for stroke prediction is covered. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing Jun 22, 2021 · Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals. Jun 12, 2024 · This code provides the Matlab implementation that detects the brain tumor region and also classify the tumor as benign and malignant. Article PubMed PubMed Central Google Scholar 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. 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 magnetic resonance imaging (MRI) brain tumor images must be physically analyzed in this work. Public Full-text 1 The objective of this research to develop the optimal model to predict brain stroke using Machine Learning Algorithms (MLA's), namely Logistic Regression (LR Nov 11, 2024 · Ischemic stroke is a major global health problem since it ranks second among the leading causes of death and disability due to cerebrovascular diseases around the world. However, while doctors are analyzing each brain CT image, time is running Feb 4, 2022 · The survivors of a stroke have a similar condition since they must relearn the lost skills when their brain is hit by a stroke. Ischemic Stroke, transient ischemic attack. In recent years, some DL algorithms have approached human levels of performance in object recognition . Machine learning algorithms are 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. A. Jan 1, 2021 · The use of deep learning, artificial intelligence, and convolutional neural network (Neethi et al. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. Using deep learning algorithms, within a short duration time can be able to identify the stroke for the patients. 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. Key Words: Stroke prediction, Machine learning, Artificial Neural Networks, Naïve Bayes and Comparative Analysis 1. A physiotherapist employs many therapies, including nerve reeducation, task coaching, and muscle strengthening to restore the mobility needs of everyday life. 4 , 635–640 (2014). 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. , 2022; Gautam and Raman, 2021) based methods in the diagnosis of brain diseases such as Alzheimer Jan 4, 2024 · Ashrafuzzaman M, Saha S, Nur K. [8] L. 53%, a precision of 87. Deep learning-based stroke disease prediction system using real-time bio signals. In particular, two types of convolutional neural network that are LeNet [2] and SegNet are used. As a result, early detection is crucial for more effective therapy. using 1D CNN and batch published in the 2021 issue of Journal of Medical Systems. Early detection is crucial for effective treatment. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. 9. 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 . Brain stroke has been the subject of very few studies. Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. 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. 3. . The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. By using four Pre–trained models such as ResNet-50, Vision Transformer (Vit), MobileNetV2 and VGG-19, we obtained our desired results. 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. 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. Sensors 21 , 4269 (2021). Prediction and Classification: The CNN model processes the extracted features to predict the likelihood of brain stroke. I. Stroke, also known as brain attack, 2021; Quandt et al Oct 1, 2024 · Download Citation | On Oct 1, 2024, Most. 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. It is much higher than the prediction result of LSTM model. Classifying the mechanism of acute ischemic stroke is therefore fundamental for treatment and secondary prevention. 1-3 Deprivation of cells from oxygen and other nutrients during a stroke leads to the death of Using CNN and deep learning models, this study seeks to diagnose brain stroke images. ResNet's residual connections aid in training deeper layers effectively, improving model performance by capturing complex spatial relationships. Yan, DT, RF, MLP, and JRip for the brain stroke prediction model. 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 Dec 26, 2021 · This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. The performance of our method is tested by Nov 21, 2024 · This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. May 12, 2021 · Bentley, P. Mathew and P. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. Collection Datasets We are going to collect datasets for the prediction from the kaggle. Neurol. 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) 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. Reddy and Karthik Kovuri and J. Many such stroke prediction models have emerged over the recent years. Nov 1, 2022 · In our experiment, another deep learning approach, the convolutional neural network (CNN) is implemented for the prediction of stroke. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing Jan 1, 2021 · Stroke is caused mainly by the blockage of insufficient blood supply across the brain. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. The paper presented a framework that will start preprocessing to eliminate the region which is not the conceivable of the stroke region. Stroke Risk Prediction Using Machine Learning Algorithms. 13 Oct 1, 2024 · 1 INTRODUCTION. The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. [8] “Focus on stroke: Predicting and preventing stroke” Michael Regnier- This paper focuses on cutting-edge prevention of stroke. Available via license: Brain tumor and stroke lesions. 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. C, 2021 May 19, 2020 · In the context of tumor survival prediction, Ali et al. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. 60%, and a specificity of 89. This work is Dec 28, 2024 · Choi, Y. Anto, "Tumor detection and classification of MRI brain image using wavelet transform and SVM", 2017 International Conference on Signal Processing and Communic… Mar 1, 2023 · This opens the scope of further research for patient-wise classification on 3D data volume for multiclass classification. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. A brain tumor is an intracranial mass consisting of irregular growth of brain tissue cells. 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. Here are 7 public repositories matching this topic This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Sep 21, 2022 · DOI: 10. Jun 8, 2021 · Acute ischemic stroke is a disease with multiple etiologies. 90%, a sensitivity of 91. 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. 1159/000525222 [Google Scholar] Singh M. 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 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. Domain-specific feature extraction has proved to achieve better-trained models in terms of accuracy, precision, recall and F1 score measurement. 3. 12720/jait. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). 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 The majority of 2 previous stroke-related research has focused on, among other things, the prediction of heart attacks. 2 million new cases each year. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction Jan 20, 2023 · Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques. would have a major risk factors of a Brain Stroke. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. Seeking medical help right away can help prevent brain damage and other complications. 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. 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 []. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. [9] “Effective Analysis and Predictive Model of Stroke Disease using Classification Methods”-A. The best algorithm for all classification processes is the convolutional neural network. proposed CNN-based DenseNet for stroke disease classification and prediction based on ECG data collected using 12 leads, and they obtained 99. Eur. 1109/ICIRCA54612. Long short-term memory (LSTM), a type of Recurrent Neural Network (RNN), is well-known 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}. An early intervention and prediction could prevent the occurrence of stroke. INTRODUCTION Stroke occurs when the blood flow is restricted veins to the brain. Wang, Z. 82% testing accuracy using fine-tuned models for the correlation between stroke and ECG. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome Dec 16, 2022 · Text prediction and classification are crucial tasks in modern Natural Language Processing (NLP) techniques. Article ADS CAS PubMed PubMed Central MATH Google Scholar Nov 28, 2022 · A Brain-Computer Interface (BCI) application for modulation of plant tissue excitability for Stroke rehabilitation is completed by analyzing the information from sensors in headwear. , 2017, M and M. et al. 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 10, 2025 · Tazin T, Alam MN, Dola NN, Bari MS, Bourouis S, Monirujjaman KM (2021) Stroke disease detection and prediction using robust learning approaches. 2021; 12(6): 539?545. Further, a new Ranker Brain Hemorrhage Classification Using NN (BHCNet) system is proposed to distinguish the brain hemorrhage using head CT scan image based on Convolutional Neural Network (CNN) as shown in Figure 1. In the most recent work, Neethi et al. 99% training accuracy and 85. Public Full-text 1 Using Data Mining,” 2021. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. 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. Many studies have proposed a stroke disease prediction model Abstract: Brain stroke prediction is a critical task in healthcare, as early detection can significantly improve patient outcomes. 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. , 2021, Cho et al. Globally, 3% of the population are affected by subarachnoid hemorrhage… Jun 22, 2021 · This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. 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. brain stroke and compared the p A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Analyzing the performance of stroke prediction using ML classification algorithms. Md. Goyal, S. One of the greatest strengths of ML is its Jan 1, 2021 · The fusion method has been used to improve the contrast of stroke region. gkucdil nqvge sedukb nmqcx futk qfpyxaw oozp yaqni fyti qzbmx uelsbyzs bfl iurzo amvh qtsxl