Brain stroke detection using convolutional neural network and deep learning models. Du Signal 2021, 38, 1727–1736.

Brain stroke detection using convolutional neural network and deep learning models 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. Detection with dual Oct 1, 2022 · In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke. Aug 1, 2022 · In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke Using CNN and deep learning models, this study seeks to diagnose brain stroke images. “Brain stroke detection using convolutional neural network and deep learning models,” in 2019 2nd International conference on intelligent communication and computational techniques (ICCT), Manipal University, Jaipur, September 28-29, 2019 (IEEE), 242–249. The utilization of deep learning techniques, particularly convolutional neural networks (CNNs) and U-Net-based models has shown great promise in accurately and automatically segmenting ischemic stroke lesions from medical imaging data. 7% sensitivity and a 0. A four- layer deep learning models for brain stroke diagnosis as well as other MRI-based machine learn- ing techniques. Dec 1, 2020 · The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction Sep 21, 2022 · The paper puts forth the capsule neural network, the machine learning system that can be trained using a less number of dataset unlike convolutional neural network and is sturdy against the IV. This further assists in understanding the relevance of the two-deep neural network components in medical image analysis namely Convolutional Neural Network (CNN) and Fully Convolutional Network (FCN). However, it is observed from empirical study that model scaling has potential to improve performance of CNN based models. The proposed methodology is to Jan 1, 2021 · PDF | On Jan 1, 2021, Khalid Babutain and others published Deep Learning-enabled Detection of Acute Ischemic Stroke using Brain Computed Tomography Images | Find, read and cite all the research Nov 18, 2022 · Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit May 22, 2024 · Developing precise models for stroke detection using decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation Oct 1, 2022 · In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke. The proposed methodology is to classify brain stroke MRI images into normal and abnormal images and delineate abnormal regions using semantic segmentation [4]. . Trait. Recently, deep learning technology gaining success in many domain including computer vision, image recognition 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. Int. Imaging Syst. For the last few decades, machine learning is used to analyze medical dataset. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. They acquired an 84. Technol. These models extract hierarchical features from MRI, CT, and X-ray images to classify stroke types[1]. Stroke Detection Using Deep Learning: A Systematic Literature Review; Proceedings of Using CNN and deep learning models, this study seeks to diagnose brain stroke images. In addition, three models for predicting the outcomes have been and Deep Convolutional Neural Network [11]. 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 In this article, we introduce a system that utilizes deep learning in the form of fusing transformer-based and convolutional neural network (CNN)-based features and few-shot learning techniques to segment ischemic strokes in multimedia MRIs. Through this study, a strategy for identifying brain stroke disease using deep learning techniques and image preprocessing is provided. The suggested method uses a Convolutional neural network to classify brain stroke images into normal and pathological categories. Article Google Scholar Khan MSI, Rahman A, Debnath T, Karim MR, Nasir MK, Band SS, Dehzangi I et al. Mar 8, 2024 · Brain-Stroke-Detection (Using Deep Learning) This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. In this study, we present a novel DCNN model for the early detection of brain stroke using CT scan images. After the stroke, the damaged area of the brain will not operate normally. [Google Scholar] Gaidhani, B. Sadia Anjum, Lal Hussain, Mushtaq Ali, Monagi H. focuses on diagnosing brain stroke from MRI images using convolutional neural network (CNN) and deep learning models. Feb 14, 2024 · In this study, we employ three categories of deep learning object identification networks: deep convolutional neural network (DCNN), you only look once (YOLO) 5, and single-shot detector (SSD). Second, we aim to evaluate the model’s performance, focusing on accuracy and sensitivity. The goal of this project is to use neural networks to create a reliable and effective method for brain stroke detection. May 15, 2024 · When it comes to finding solutions to issues, deep learning models are pretty much everywhere. Sep 29, 2019 · This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. A convolutional deep network architecture is proposed with an optimized dimensional U-Net (D-UNet) by blocking and adaptively sequencing the Deep Learning based Brain Stroke Detection using Improved VGGNet SRISABARIMANI K. ABSTRACT: This project utilizes deep learning models like CNN, SVM, and VGG16 to accurately classify brain stroke images. 637 - 646 Crossref View in Scopus Google Scholar Jan 1, 2017 · Request PDF | On Jan 1, 2017, Aneta Lisowska and others published Thrombus Detection in CT Brain Scans using a Convolutional Neural Network | Find, read and cite all the research you need on Abstract: For the last few decades, machine learning is used to analyze medical dataset. Convolutional neural networks (CNNs) have been extensively utilized in segmentation architectures like U-Net [7]. Nov 13, 2023 · Over the past two decades, numerous deep learning (DL) neural network models, including convolutional neural networks (CNNs), have been developed and extensively utilized in classification Dec 9, 2022 · PDF | On Dec 9, 2022, Naufal Riz Kifli and others published Brain Stroke Classification using One Dimensional Convolutional Neural Network | Find, read and cite all the research you need on In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke. Brain stroke detection using convolutional neural network and deep learning models. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. Moreover, the Brain Stroke CT Image Dataset was used for stroke classification. cmpb. [3] survey studies on brain ischemic stroke detection using deep learning Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. integrated wavelet entropy-based spider web plots and probabilistic neural networks to classify brain MRI, which were normal brain, stroke, degenerative disease, infectious disease, and brain tumor in their study. Sona4, E. Menaka and Annie Johnson and Sundar Anand Jun 21, 2024 · This article introduces and evaluates a novel Deep Neural Network (DNN) designed specifically for brain stroke detection. However, existing DCNN models may not be optimized for early detection of stroke. INTRODUCTION Globally, stroke is a leading cause of death, accounting for Mar 1, 2024 · Key points: • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains Apr 4, 2023 · Brain MRI is one of the medical imaging technologies widely used for brain imaging. According to Ardila et al. Jan 1, 2024 · Brain stroke detection using convolutional neural network and deep learning models 2019 2nd International conference on intelligent communication and computational techniques , ICCT) ( 2019 ) , pp. The model has a classification accuracy of 89. The best algorithm for all classification processes is the convolutional neural network. Gasimova A et al (2018) Learning attention from multi-modal imaging and text. Keywords—Acute ischemic brain stroke; deep learning; convolutional neural network; CT brain slice classification; brain tissue segmentation; brain tissue contrast enhancement; brain tissue classification I. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Dec 1, 2023 · This study has explored the recent advancements in ischemic stroke segmentation using deep learning models. Sep 1, 2019 · Through experimental results, it is found that deep learning models not only used in non-medical images but also give accurate result on medical image diagnosis, especially in brain stroke detection. 2020. A convolutional deep network architecture is proposed with an optimized dimensional U-Net (D-UNet) by blocking and adaptively sequencing the The followed approach is based on the usage of a 3D Convolutional Neural Network (CNN) in place of a standard 2D one. Sep 1, 2019 · In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. slices in a CT scan. The dataset was processed for image quality, split into training, validation, and testing sets, and evaluated using accuracy, precision, recall, and F1 score. Convolutional neural networks (CNNs) are particularly good at analyzing images like MRI or CT scans , which help them identify subtle patterns that could indicate a higher risk of stroke. 00%. As a result, early detection is crucial for more effective therapy. The ResNet and VGG-16 obtained an accuracy of 92% and 81% respectively. Int J Imag Syst Technol 2022;32(1):307–323. Oct 11, 2023 · In this study, we employ three categories of deep learning object identification networks: deep convolutional neural network (DCNN), you only look once (YOLO) 5, and single-shot detector (SSD). compbiomed. Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit Apr 1, 2023 · 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 Jan 31, 2025 · Objective The study aims to develop a novel Multilayer Perceptron of Convolutional Neural Network-based Residual Network (MLPCNNbRN) for early brain stroke detection, focusing on improving the Nov 27, 2024 · The goals of our work are manifold. In addition, three models for predicting the outcomes have been developed. To classify the images, the pre- BRAIN STROKE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS Akshaya M D1, Farhan N1, Sreelakshmi S P1,Anandhu Uday1,Mithun Vijayan2 1Student , Department of Electronics & Communication Engineering 2Asst. 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. It hints at other possible deep architectures that can be proposed for better results towards stroke lesion detection. Application for Lesion Localisation in DWI. Augmentation techniques are applied to increase dataset diversity, such as rotating, flipping, or zooming images, enhancing model generalization. J. g. The chapter is arranged as follows: studies in brain stroke detection are detailed in Part 2. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. RNNs and LSTMs are powerful tools for analyzing Apr 2, 2024 · Malathi M, Sinthia P (2019) Brain tumour segmentation using convolutional neural network with tensor flow. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Jan 20, 2023 · Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques January 2023 European Journal of Electrical Engineering and Computer Science 7(1):23-30 Apr 27, 2024 · In recent years, deep convolutional neural network (DCNN) models have shown great promise in the automated detection of brain stroke from CT scan images. Aug 1, 2022 · DOI: 10. Dec 1, 2024 · A new ensemble convolutional neural network (ENSNET) model is proposed for automatic brain stroke prediction from brain CT scan images. R. Following preprocessing and model tuning, it achieves high accuracy in detecting stro Mar 30, 2024 · Unlike machine learning, which relies on fundamental concepts, deep learning employs artificial neural networks that mimic human thinking and learning processes. Mohana Sundaram1, G. 105941 Corpus ID: 251915718; Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks @article{Yaln2022BrainSC, title={Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks}, author={Sercan Yalçın and Huseyin Afsin Vural}, journal={Computers in biology and This project firstly aims to classify brain CT images into two classes namely 'Stroke' and 'Non-Stroke' using convolutional neural networks. II. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. Jun 21, 2024 · Two distinct deep learning models are employed to analyze the CT images: a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. 2019. This system has the potential to aid medical professionals in timely diagnosis and treatment, ultimately improving patient outcomes. 22. Deep neural networks have achieved state-of-the-art results in numerous computer vision tasks, including medical image segmentation, by learning intrinsic patterns in a data-driven manner [6]. 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. 57% and a training accuracy of 97% without data augmentation. Abhilash3, K. A clinical support system for brain tumor classification using soft computing Jan 1, 2022 · Sadia Anjum, Lal Hussain, Mushtaq Ali, Monagi H. , Samadhan (2019). - shafoora/BRAIN-STROKE-CLASSIFICATION-BASED-ON-DEEP-CONVOLUTIONAL-NEURAL-NETWORK-CNN- Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. Keywords Brain stroke, convolutional neural network, residual network, stroke detection, multilayer identifies brain strokes using a convolution neural network. 00% and a validation accuracy of 98. (2022) Accurate brain tumor detection using deep convolutional neural network. To classify the images, the pre- The most common segmentation models are Convolutional Neural Networks (CNNs). However, while doctors are analyzing each brain CT image, time is running Dec 2, 2024 · Different types of deep learning models are used to predict stroke risk by utilizing various data types. 32(1):307–323. Karthik and R. INTRODUCTION Globally, stroke is a leading cause of death, accounting for around 15 million deaths annually [1], [2]. ; Rajamenakshi, R. 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). Oct 1, 2024 · Therefore, in thus study, several deep neural network models, including DenseNet121, ResNet50, Xception, MobileNet, VGG16, and EfficientNetB2 are proposed for transfer learning to classify MR Detection of Brain Stroke Using Machine Learning Algorithm K. Haritha2, A. Bharath kumar6 Department of Electronics & Communication Engineering Siddharth Institute of Engineering & Technology (Autonomous), Puttur-517583, Andhra Pradesh. The approach involves classifying stroke MRI images as normal or abnormal, using three types of CNN models: ResNet, MobileNet, and VGG16. Feb 27, 2025 · Gaidhani BR et al (2019) Brain stroke detection using convolutional neural network and deep learning models. The proposed DCNN model consists of three main features and residual network learning, offering a more accurate and reliable approach than previous methods. Du Signal 2021, 38, 1727–1736. APJ Abdul kalam technological university, kerala, india Jul 2, 2024 · Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model. (2021), "Deep Convolutional Neural Networks for Brain Stroke Detection in CT Aug 1, 2020 · Critical case detection from radiology reports is also studied, yet with different grounds. Sep 15, 2024 · Addressing challenges arising from a limited dataset and computing resources, we implemented transfer learning and image augmentation techniques. We discovered that deep learning models Jul 4, 2024 · 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. First, in the pre-processing stage, they used two dimensional (2D) discrete wavelet transform (DWT) for brain images. we proposed certain advancements to well-known deep learning models like VGG16, ResNet50 and DenseNet121 for Dec 31, 2021 · A novel deep residual attention convolutional neural network (DRANet) is proposed to accurately and simultaneously segment and quantify ischemic stroke and WMH lesions in the MRI images. D. 2019 2nd International conference on intelligent communication and computational techniques (ICCT), IEEE. The proposed CAD-BSDC technique aims in classifying the provided MR brain image as normal or abnormal. We propose a fully automatic method for acute ischemic stroke detection on brain CT scans. Aug 26, 2020 · DOI: 10. R. The complex 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. As a result, deep learning has become an integral part of the medical industry, renowned for its ability to accurately and swiftly detect strokes. ; Sonavane, S. Jan 10, 2025 · Deep learning methods have shown promising results in detecting various medical conditions, including stroke. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to in CT Images Using Convolutional Neural Networks": For the purpose of brain nodule detection on CT scans, the authors suggested a CNN- based method. A convolutional deep network architecture is proposed with an optimized dimensional U-Net (D-UNet) by blocking and adaptively sequencing the Convolutional Neural Network (CNN) based deep learning models are being widely used for medical image analysis. 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 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 🧠 Advanced Brain Stroke Detection and Prediction System 🧠 : Integrating 3D Convolutional Neural Networks and Machine Learning on CT Scans and Clinical Data Welcome to our Advanced Brain Stroke Detection and Prediction System! This project combines the power of Deep Learning and Machine Jan 24, 2023 · Rapid assessment of acute ischemic stroke by computed tomography using deep convolutional neural networks Journal of Digital Imaging , 34 ( 3 ) ( 2021 ) , pp. Abstract: Brain stroke prediction is a critical task in healthcare, as early detection can significantly improve patient outcomes. Keywords—Acute ischemic stroke; deep brain learning; convolutional neural network; CT brain slice classification; brain tissue segmentation; brain tissue contrast enhancement; brain tissue classification I. Nov 28, 2022 · In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer’s disease (AD), brain tumor, epilepsy, and Parkinson . ARTHI R Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus Chennai Tamilnadu, INDIA Abstract: - Brain stroke is one of the critical health issues as the after effects provides physical inability and Dec 1, 2021 · A total of 239 T1-weighted MRI scans of chronic ischemic stroke patients from a public dataset were retrospectively analyzed by 3D deep convolutional segmentation models with residual learning Nov 18, 2022 · Presently, machine learning (ML) and deep learning (DL) models can be extremely utilized for disease detection and classification processes. The rest of this paper is organized as follows. 2022. 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. personnel with automated and more accurate tools. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The C NN model architecture , chosen for its powerful image processing capabilities, achieves a remarkable training accuracy of 99. A convolutional deep network architecture is proposed with an optimized dimensional U-Net (D-UNet) by blocking and adaptively sequencing the Several methods have been proposed to detect ischemic brain stroke automatically on CT scans using machine learning and deep learning, but they are not robust and their performance is not ready for clinical practice. METHODOLOGY Convolution Neural Network: Convolutional Neural Networks (CNNs) represent a class of deep learning models specifically crafted for tasks Applications of deep learning in acute ischemic stroke imaging analysis. 1016/j. Divya sri5, C. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. 242 - 249 Sep 24, 2023 · Gaidhani, Bhagyashree Rajendra, Rajamenakshi, R. Alkinani, Wajid Aziz, Sabrina Gheller, Adeel Ahmed Abbasi, Ali Raza Marchal, Harshini Suresh, and Tim Q. The deep learning techniques used in the chapter are described in Part 3. In particular, two types of convolutional neural network that are LeNet [2] and SegNet are used. 2022. 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. The study developed CNN, VGG-16, and ResNet-50 models to classify brain MRI images into hemorrhagic stroke, ischemic stroke, and normal . In this work, a methodology for scaling CNN based models in all dimensions suchas depth, width and resolution has been proposed. DEEP LEARNING ARCHITECTURES FOR BRAIN STROKE DETECTION AND PREDICTION 1. 89 per scan false positive rate. The ensemble May 30, 2023 · Anjum S, Hussain L, Ali M, Alkinani MH, Aziz W, Gheller S, et al. In this article, a novel computer aided diagnosis (CAD) based brain stroke detection and classification (CAD-BSDC) model has been developed for MRI images. Vol. , and Sonavane. First, we aim to demonstrate how Federated Learning can enhance stroke detection and prediction using Deep Learning, compared with other approaches. Oct 1, 2022 · In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke. Prof , Department of Electronics & Communication Engineering Dr. In the second stage, the task is making the segmentation with Unet model. 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. Medical image data is best analysed using models based on Convolutional Neural Networks (CNNs). Arasi PRE, Suganthi M. May 23, 2024 · In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke Nov 19, 2023 · The results obtained show that Deep Learning models outperformed the Machine Learning models, moreover the DenseNet-121 provided the best results for brain stroke prediction with an accuracy of 96%. Asian Pac J Cancer Prev APJCP 20(7):2095. Jan 1, 2023 · In this chapter, deep learning models are employed for stroke classification using brain CT images. Convolutional Neural Networks (CNNs) Convolutional Neural Networks (CNNs) are commonly utilized for identifying strokes in medical imaging. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. Detecting brain tumors using deep learning convolutional neural network with transfer learning approach. The rationale behind using all Dec 1, 2020 · The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. Duong. Highlighting the innovation of using a weighted Binary Cross Entropy (BCE) loss function to address dataset imbalances, this work also aims to propose a less computationally complex model that makes predictions using very basic demographic and health-related information. Section 3 presents the proposed approach, models, and algorithm. Therefore, the aim of the early identification of ischemic stroke on brain CT scans. 105728 Corpus ID: 221496546; Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects @article{Karthik2020NeuroimagingAD, title={Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects}, author={R. For example, Karthik et al. 9%, according to our findings. 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. ENSNET is the average of two improved CNN models named InceptionV3 and Xception. In the context of brain tumor detection, a Convolutional Neural Network (CNN) was utilized, achieving a validation accuracy of 78. Saritha et al. Nov 13, 2023 · Over the past two decades, numerous deep learning (DL) neural network models, including convolutional neural networks (CNNs), have been developed and extensively utilized in classification applications to efficiently detect and segment organs and tissues, such as brain lesions, surpassing conventional methods 11,18,19. ysnonzw vwwllbm qppxa tztbu tudx ogdjy nzmrr rbmepyx lzg hduwhyp otu bmg nbkh arxkd clz