Brain stroke detection system based on ct images using deep learning Dec 6, 2024 · It is through stroke that disability and mortality are caused in most populations worldwide; therefore, fast detection and accuracy for timely intervention are required. Early detection is crucial for effective treatment. With the advancements of deep learning, the detection of brain strokes from CT images becomes possible. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Early detection of strokes and their rapid intervention play an important role in reducing the burden of disease and improving clinical outcomes. Computer aided diagnosis model for brain stroke classification in MRI images using machine learning algorithms. 1% sensitivity= 97% FScore= 98% ACC= 98%: Enhanced stroke classification and diagnosis. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. …” 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. This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. The chapter is arranged as follows: studies in brain stroke detection are detailed in Part 2. ResNet's residual connections aid in training deeper layers effectively, improving model performance by capturing complex spatial relationships. Medical image Jan 24, 2023 · Han et al. The study shows how CNNs can be used to diagnose strokes. Deep learning is also used in vision assistance and image watermarking . 99. A hybrid image enhancement based brain MRI images classification technique. , Civit A. Jun 22, 2021 · Deep Learning-Based Stroke Disease Prediction System Using Real-T ime Bio Signals Yoon-A Choi 1 , Se-Jin Park 2 , Jong-Arm Jun 3 , Cheol-Sig Pyo 3 , Kang-Hee Cho 4 , Han-Sung Lee 5, * Sep 24, 2023 · The keywords used for the specific search were [“brain stroke” AND (“machine learning” OR “deep learning”)]. 108 total papers were identified which were selected based on their relevance to align with the objectives of the proposed study. , Luna-Perejón F. This work describes a robust paradigm for inferring strokes from CT scans using deep reinforcement learning and image analysis. 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 normal and as stroke. Also, due to its computational and storage needs, 3D CNN has been largely avoided. 42% and an AUC of 0. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. This study has achieved good classification outcomes than conventional approaches. In the second stage, the task is making the segmentation with Unet model. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate 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 Jun 26, 2024 · Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. In this paper, we propose a classification and segmentation method using the enhanced D-UNet deep learning method, which is an encoder and decoder CNN-based deep learning model developed on brain CT images. The complex stroke detection system based on CT images of the brain coupled with a genetic algorithm and a bidirectional long short-term Memory (BiLSTM) to detect strokes at a very early stage. Sci. The purpose of this paper is to develop an automated Sep 1, 2019 · Deep learning and CNN were suggested by Gaidhani et al. Oct 1, 2023 · Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images Complex & Intelligent Systems , 7 ( 2021 ) , pp. Each year, according to the World Health Organization, 15 million people worldwide Fig. [Google Scholar] Associated Data Deep learning techniques with VGG-16 architecture and Random Forest algorithm are implemented for detecting hemorrhagic stroke using brain CT images under segmentation. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. developed an automatic intracranial hemorrhage detection model based on deep learning, with a sensitivity of 0. 🛒Buy Link: https://bit. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. [Google Scholar] 16. 143:109922. The Optimized Deep Learning for Brain Stroke Detection approach (ODL-BSD) was put forth. serious brain issues, damage and death is very common in brain strokes. Jan 1, 2023 · In this chapter, deep learning models are employed for stroke classification using brain CT images. Simulation analysis using a set of brain stroke data and the approaches. 1109/ACCESS. used one-stage lesion detection to detect different lesions in CT images. 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. Dec 1, 2020 · The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. Feb 27, 2025 · Takahashi N et al (2019) Computerized identification of early ischemic changes in acute stroke in noncontrast CT using deep learning. 00. in deep learning-based stroke detection also uses neuroimaging techniques [24] Particularly in the neuroimaging domain, research efforts focus on applying deep learning to perform clinical tasks such as imagebased stroke detection [3], Magenetic Resonance-Computed Tomography (MR-CT) modality transfer [4] and detection of neurodegenerative diseases [5]. 1. The Brain Stroke detection model hada 73. used CT images for detecting the infarct core using a 2D patch-based deep learning model [101]. International Journal of Advanced Science and Technology . 2. Vankdothu R, Hameed MA, Fatima H. 3: Sample CT images a) ischemic stroke b) hemorrhagic stroke c) normal II. This Over the past few years, stroke has been among the top ten causes of death in Taiwan. , Aboalsamh H. Electr. The effectiveness of the approach was proved by achieving 97% accuracy in categorizing lung data and 97% Dice coefficient in segmentation, which confirms the promise of the system in targeting. The proposed methodology is to Strokes damage the central nervous system and are one of the leading causes of death today. Oct 1, 2020 · Several studies have focused on various CT examinations, including deep learning (DL)-based detection of hemorrhagic lesions on brain CT images and segmentation [12], and distinguishing COVID-19 The environments in which the two deep learning models were developed and implemented are detailed in Table II. The system’s first component is a brain slice Dec 19, 2024 · High-Accuracy Stroke Detection System Using a CBAM-ResNet18 Deep Learning Model on Brain CT Images Stroke is a brain dysfunction that occurs suddenly as a result of local or overarching damage to the brain, lasts for at least 24 hours, and causes about 15 million deaths each year globally. This deep learning-based system The followed approach is based on the usage of a 3D Convolutional Neural Network (CNN) in place of a standard 2D one. Dec 22, 2023 · When vessels present in brain burst or the blood supply to the brain is blocked, brain stroke occurs in human body. We propose a fully automatic method for acute ischemic stroke detection on brain CT scans. Ullah I. The system is developed using Python for the backend, with Flask serving as the web framework. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. When we classified the dataset with OzNet, we acquired successful performance. Over the past few years, stroke has been among the top ten causes of death in Taiwan. 2020;29(5):7976–7990. Grewal et al. IMPLEMENTATION DETAILS Deep Learning Manikandan S. 2022. Hilbert et al. , Hussain M. Compared with several kinds of stroke, hemorrhagic and ischemic causes have a negative impact on the human central nervous system. However Nov 27, 2024 · They proposed a real-time stroke detection system based on Deep Learning with utilization of Federated Learning to enhance accuracy and privacy preservation. 2 and Dec 1, 2023 · In recent years, deep learning-based approaches have shown great potential for brain stroke segmentation in both MRI and CT scans. JPPY2404 – Brain Stroke Detection System based on CT images using Deep Learning ₹ 10,000. Images No. 7% respectively. [13] included 578 brain CT images, 463 of which were stroke images, and obtained An early stroke detection system based on CT images of the brain coupled with a genetic algorithm and a bidirectional long short-term Memory to detect strokes at a very early stage is developed and physicians can make an informed decision about stroke. The purpose of this paper is to gather information or answer related to this paper’s research question Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. Robben et al. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. The integration of these technologies results in a sophisticated brain stroke detection system that not only boasts high accuracy but also promises scalability and practical utility in clinical settings. Automatic brain stroke diagnosis based on supervised learning is possible with the help of several datasets. Aug 1, 2020 · Critical case detection from radiology reports is also studied, yet with different grounds. 8864 and a precision of 0. Yap et al. Nov 1, 2017 · In this thesis, a deep learning-based system was designed to assist radiologists in the process of detecting COVID-19 disease from chest computed tomography images. 77%. 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. Moreover, the brain hemorrhage CT image dataset is exploited for hemorrhage detection. This project demonstrates the potential of deep learning in medical diagnostics, offering a tool that can significantly aid Mar 8, 2024 · Addressing these challenges systematically will contribute to the successful development and deployment of this brain stroke image classification system using Flask, OpenCV, TensorFlow, Scikit-learn, Matplot, Seaborn and Keras. 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. May 15, 2024 · Download Citation | Stroke detection in the brain using MRI and deep learning models | When it comes to finding solutions to issues, deep learning models are pretty much everywhere. 8. In order to diagnose and treat stroke, brain CT scan images Mar 16, 2021 · Deep learning also serves as predictor in medical examinations of infarct brain volume and online stroke detection . Deep learning networks are commonly employed for medical image analysis because they enable efficient computer-aided diagnosis. It contains 6000 CT images. methods [45] DBSCAN, hierarchical using CT images of the brain [57] SCM, SVM Machine Learning Model: CNN model built using TensorFlow for classifying brain stroke based on CT scan images. RELEVANT WORK The majority of strokes are seen as ischemic stroke and hemorrhagic stroke and are shown in Fig. Jun 10, 2024 · Brain Stroke Detection System based on CT images using Deep Learning | Python IEEE Project 2024 - 2025. One of the techniques for early stroke detection is Computerized Tomography (CT) scan. Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. Therefore, this paper first chooses Faster R-CNN as the lesion detection network in brain MRI images of ischemic stroke. achieved a classifier performance of up to 98. Dec 4, 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 May 22, 2024 · Objectives Artif icial intelligence (AI)–based image analysis is increasingly applied in the acute stroke field. For image A highly non-linear scale-invariant deep brain stroke detection model, integrating networks like VGG16, network-in-network layer, and spatial pyramid pooling layer (BSD-VNS), is implemented with attributes of the SPP layer that progresses with any gauge of brain stroke measurement. However, it is not clear which modality is superior for this task. However, the drive towards developing better system for brain stroke detection is still in progress. The pre-trained ResNetl01, VGG19, EfficientNet-B0, MobileNet-V2 and GoogleNet models were run with the same dataset and same parameters. Shin et al. 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. Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and treatable Jan 1, 2023 · Sample CT images of (a) ischemic, (b) hemorrhagic stroke and (c) normal brain Red area marks the location of ischemic and hemorrhagic lesions on the images Flowchart of the proposed system Nov 1, 2017 · The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm and can effectively assist the doctor to diagnose. Materials and methods 3. Vol. The improved model, which uses PCA instead of the genetic algorithm (GA) previously mentioned, achieved an accuracy of 97. 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. Brain CT IV. Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images. Therefore, the aim of 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. Med. This is to detect brain stroke from CT scan image using deep learning models. Comput Med Imaging Graph 78:101673 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. By using a collection of brain imaging scans to train CNN models, the authors are able to accurately distinguish between hemorrhagic and ischemic strokes. Dec 8, 2022 · After the stroke, the damaged area of the brain will not operate normally. However, while doctors Dec 9, 2024 · In this study, a real-time system has been developed for the detection and segmentation of strokes in brain CT images using YOLO-based deep learning models. After the stroke, the damaged area of the brain will not operate normally. Mar 25, 2022 · Brain computed tomography (CT) is commonly used for evaluating the cerebral condition, but immediately and accurately interpreting emergent brain CT images is tedious, even for skilled neuroradiologists. firstly established a three-tier diagnostic tool using machine learning and deep learning that was based on the structured clinical data with nonstructured NCCT imaging data for LVO diagnosis, which achieved superior performance with the AUC of 0. , Dhanalakshmi P. In recent years, machine learning methods have attracted a lot of attention as they can be used to detect Jun 22, 2021 · Currently, many deep learning-based studies use CT or MRI images to detect stroke [26,27,28,29,30,31,32]. Materials a) Data Set A data set is a collection of data. This study offers a novel neural network-based method for brain stroke identification. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. [5] as a technique for identifying brain stroke using an MRI. Moreover, it was also shown in Mostapha and Styner that deep learning techniques may have an impact on medical examinations of humans in various age, also A brain stroke is a serious medical illness that needs to be detected as soon as possible in order to be effectively treated and its serious effects avoided. A two-step light-weighted convolution model is proposed by using the data collected from multiple- repositories to inscribe this constraint. ₹ 5,000. [7] The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" 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 • The dataset offers professional markups of standard brain strokes. 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. Better methods for early detection are crucial due to the concerning increase in the number of people suffering from brain stroke. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. Civit-Masot J. 3369673. A brain tumor identification and classification using deep learning based on CNN-LSTM method. 3390/app10134640. density-based outlier detection. The results obtained demonstrated that the DenseNet-121 classifier performs the best of all the selected algorithms, with an accuracy of 96%, Recall of 95. The deep learning techniques used in the chapter are described in Part 3. Hypotheses. Talo M et al (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. Moreover, the Brain Stroke CT Image Dataset was used for stroke classification. Normal Stroke Fig2. An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Jan 10, 2025 · In , the authors presented a Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet. This paper provides a comprehensive review of recent advancements in the use of deep learning for stroke lesion segmentation in both MRI and CT scans. This paper suggests an early stroke detection system based on CT brain images with a genetic algorithm for feature selection and a BiLSTM model for classification. The suggested system makes use of deep learning techniques to evaluate medical imaging data, 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. On the other hand, Vamsi et al. There are two types of strokes, which is ischemic and hemorrhagic. Appl. Mar 29, 2024 · kind of system where they used brain CT image as input along with some pre-possessing and classified it with CNN. However, the location of ischemic stroke in the CT image is not obvious, so the diagnosis need to rely on doctors to assess the image. Computed tomography (CT) images supply a rapid Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. 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 research works are evolved with better solutions. 35754-35764, 2024, doi: 10. 60 % accuracy. 929 - 940 Crossref View in Scopus Google Scholar Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and treatable. 2% and precision of 96. Medical image data is best analysed using models based on Convolutional Neural Networks (CNNs). As a result, early detection is crucial for more effective therapy. ly/3XUthAF(or)To buy this proj Nov 18, 2022 · To evaluate the detection outcomes, a board-certified radiologist assessed the testing set head CT image with and without help of detection system . For example, in a study classifying hemorrhagic stroke and ischemic stroke using brain CT images, Gautam et al. The study "Deep learning-based classification and regression of interstitial Brain Strokes on CT" by H. One of the cerebrovascular health conditions, stroke has a significant impact on a person’s life and health. 00 Original price was: ₹10,000. Also, based on worldwide standards of clinical significance, it gives details about each image in the database's region, type, and disease severity level of stroke. The main objective of the study is to provide fast and accurate detection of hemorrhagic and ischemic strokes, thus assisting healthcare professionals in clinical decision-making processes. Its implementation for the detection and quantification of hemorrhage suspect TABLE I. examined DL methods to build model to directly forecast better reperfusion afterward endovascular treatment (EVT) and better functional outcomes using CT images. This project is developing an advanced brain stroke detection system based on a combination of medical imaging and machine learning algorithms. 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. pretrained on the ImageNet dataset and used the prior information of natural images for breast tumor detection. . This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. Nevertheless, the Mar 30, 2024 · Computerized tomography (CT) scan image-based stroke classification is a well-known area of stroke detection. Brain stroke MRI pictures might be separated into normal and abnormal images Apr 10, 2021 · The medical image lesion detection and auxiliary diagnosis system based on deep learning can extract the advanced features of the lesion in the medical image, and the combination with clinical practice will greatly reduce the workload of doctors. Patients Oct 1, 2020 · Classification of hemorrhagic and ischemic strokes by SCM technique using CT images of the brain [57] SCM, SVM, multilayer perceptron, minimal learning machine, LDA: 300: CT images: SCM: specificity= 99. For example, Karthik et al. It uses data from the CT scan and applies image processing to extract features such as ischemic areas, hemorrhagic regions, and perfusion deficits. However, while doctors are analyzing each brain CT image, time is running 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. Medical Imaging 2019: Computer-Aided Diagnosis, SPIE. III. User Interface: Tkinter-based GUI for easy image uploading and prediction. 00 Current price is: ₹5,000. This study proposed the use of convolutional neural network (CNN Nov 14, 2022 · Yu et al. - mersibon/brain-stroke-detection-with-deep-learnig Jan 20, 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. To eectively identify brain strokes using MRI data, we proposed a deep learning-based approach. Two deep learning models were developed, including the 4767 CT brain images. 2020. 8124 in a dataset of 77 brain CT images interpreted by three radiologists. In this study, they compared a deep learning-based algorithm (3D-BHCA) to 5 stroke neurologists, finding that the region-based and score-based analyses of 3D-BHCA model were superior or equal to those of stroke neurologists overall . [3] survey studies on brain ischemic stroke detection using deep learning May 30, 2023 · Ullah Z, Farooq MU, Lee S-H, An D. Both of this case can be very harmful which could lead to serious injuries. 12, pp. They used the mRMR approach to minimize the size of the features from 4096 to 250 after obtaining 4096 relevant features from OzNet's fully linked layer and achieved a stroke detection accuracy from brain CT scans of 98. 2024. Their methodology involves utilizing and evaluating YOLOv8 models on comprehensive datasets, containing images of individuals with and without strokes, with a focus on detecting facial Aug 1, 2023 · Machine learning methods especially neural-network based algorithms have shown huge success in medical image analysis for variety of tasks including the detection, segmentation and classification of brain tumors and Ischemic stroke. [36] proposed a deep learning approach for stroke classification and lesion segmentation on CT images based on the use of deep models [37]. 101:107960 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 Oct 12, 2023 · Imaging is needed in stroke cases in order to understand what the type of stroke (ischemic, hemorrhagic) is, to rule out bleeding, to determine the infarct area and to plan treatment. The two models work as two-step deep learning models to classify brain normal, ischemic, and hemorrhagic conditions by model 01, while acute, subacute, 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 Nov 19, 2023 · The proposed work aims to develop a model for brain stroke prediction using MRI images based on deep learning and machine learning algorithms. Aug 7, 2024 · Brain stroke is one of the most common causes of death, ranking as the second leading cause worldwide. Jul 28, 2020 · Deep learning iot system for online stroke detection in skull computed. Visualization: Includes model performance metrics such as accuracy, ROC curve, PR curve, and confusion matrix. 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 This project firstly aims to classify brain CT images into two classes namely 'Stroke' and 'Non-Stroke' using convolutional neural networks. [ 16 ] developed such kind of system where they used brain CT image as input along with some prepossessing and classified it with CNN. This model does not For the last few decades, machine learning is used to analyze medical dataset. Identification: A Machine Learning-Based Diagnostic Model Using Neuroimages," in IEEE Access, vol. Apr 10, 2021 · Cai et al. Among the several medical imaging modalities used for brain imaging based on deep learning. In this study, we propose a method for classifying brain stroke images and predicting the presence of a stroke using convolutional neural networks (CNNs), which are particularly effective Jan 1, 2023 · Starting from this point, in this chapter, some of the popular deep learning models are employed for hemorrhage detection using brain CT images. 60%. The dataset used in this project is taken from Teknofest2021-AI in Medicine competition. 16 Dec 31, 2021 · Their dataset was collected from the Radiological Society of North America (RSNA). 27% uisng GA algorithm and it out perform paper result 96. 9% accuracy rate. 847, potentially improving the prehospital triage systems for AIS . Oct 1, 2022 · In 3D CNN, however, spatial information is extracted. 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 1, 2020 · Clèrigues et al. In this study, brain stroke disease was detected from CT images by using the five most common used models in the field of image processing, one of the deep learning methods. also employed a deep learning architecture to predict core and penumbra regions of the brain from acute CTP scans. However, the authors included a small dataset and detected only hemorrhagic stroke in their analysis. DATASET PREPARATION FOR EACH MODEL Model Labeling Type Labeling As Brain CT Slice Classification Image as a class Brain CT Slice 570 50 Not Brain CT Slice 560 50 Brain Tissue Segmentation Pixel as a class Not Brain Tissue 365 18 Brain Tissue Classification Image as a class Acute 300 63 Normal 300 62 Brain Tissue No. Jan 1, 2023 · In the experimental study, a total of 2501 brain stroke computed tomography (CT) images were used for testing and training. , Domínguez Morales M. In this study, Brain Stroke and other interstitial brain disorders were identified on CT images using a CNN model. 2020;10:4640. slices in a CT scan. 1. Marbun et al. Comput. It uses data from the CT scan and applies image processing to extract features Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. doi: 10. g. May 15, 2024 · When it comes to finding solutions to issues, deep learning models are pretty much everywhere. Stroke is a medical condition in which poor blood flow to the brain causes cell death and causes the brain to stop functioning properly. Eng. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. 7. The steps which are as follows: first, a large volume of high quality CT scan images will be gathered second, the pre-processing of the scan images to improve the image quality and third, an advanced CNN model will be designed for accurate stroke detection. Thus, in this research work, deep learning-based brain stroke detection system is presented using improved VGGNet. skulw uoheb rhtb xqtu quc dib xgawwyn cwap fcvhw rxz qmlwyw bbdks coywc dzws qcy