Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques

Rahman, Senjuti; Hasan, Mehedi; Sarkar, Ajay Krishno

doi:10.24018/ejece.2023.7.1.483

Cited by 29 publications

(5 citation statements)

References 11 publications

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“…In the Stroke MD stroke grouping and prediction system, the study talks about how to make an application interface for neurologists to use to manage and see related medical data [15]. The goal of the system is to make it easier to use a forecasting model to get information and add visible data.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Stroke Predictor Application

Kishore Kanna,

Rami Reddy,

Panigrahi

et al. 2024

EAI Endorsed Trans IoT

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When blood flow to the brain stops or slows down, brain cells die because they don't get enough oxygen and nutrients. This condition is known as an ischemic stroke. It is now the biggest cause of death in the whole planet. Examining the afflicted people has shown a number of risk variables that are thought to be connected to the stroke's origin. Numerous studies have been conducted to predict the illnesses associated with stroke using these risk variables. The prompt identification of various warning symptoms associated with stroke has the potential to mitigate the severity of the stroke. The utilization of machine learning techniques yields prompt and precise predictive outcomes. Although its uses in healthcare are expanding, certain research domains have a stronger need for more study. We think that machine learning algorithms may aid in a deeper comprehension of illnesses and make an excellent healthcare partner. The textual dataset of numerous patients, which includes many medical variables, is gathered for this study. The missing values in the dataset are located and dealt with during processing. The dataset is used to train machine learning algorithms including Random Forest, Decision Tree classifier, and SVM. The method that delivers the greatest accuracy for our dataset is then selected once the accuracy of the algorithms has been determined. This aids patients in determining the likelihood of a brain stroke and ensuring they get the right medical attention.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Based Stroke Predictor Application

Kishore Kanna,

Rami Reddy,

Panigrahi

et al. 2024

EAI Endorsed Trans IoT

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show abstract

“…Early identification and management are vital for improving patient outcomes due to the fact that it is a prominent cause of disability and death on a global scale (Rahman et al, 2023). Computed tomography (CT) and magnetic resonance imaging (MRI) are crucial in the diagnosis of strokes (Li et al, 2020;Xu et al, 2020;Surya et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Feature Fusion-based Brain Stroke Identification Model Using Computed Tomography Images

Abulfaraj,

Dutta,

Sait

2024

Journal of Disability Research

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Accurate and rapid diagnosis is essential in the healthcare system for the detection of strokes to mitigate the devastating effects. This study introduces an innovative model for identifying strokes using advanced deep learning (DL) architectures, including SqueezeNet v1.1 and MobileNet V3-Small, feature fusion approaches, and CatBoost models. Using SqueezeNet v1.1 and MobileNet V3-Small, the authors extract meaningful features from computed tomography images that capture local details and global patterns suggesting stroke conditions. Subsequently, they employ feature fusion to combine the complementary representations derived by both architectures, consequently boosting the discriminative capability of the feature set. The Optuna-based CatBoost model is employed to predict stroke using the fused features. The experimental findings show outstanding performance, with a considerable accuracy of 99.1%. The high accuracy level demonstrates our suggested method’s effectiveness in precisely detecting strokes from medical imaging data. Combining DL architectures, feature fusion, and gradient-boosting models offers a promising approach to enhancing stroke diagnosis systems. This can potentially improve patient outcomes and clinical decision-making in stroke treatment.

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“…The relationship between brain ageing and single nucleotide polymorphisms (SNPs) was examined in two recent researches that used data from the UK Bio bank. A convolutional neural network (CNN) model for estimating brain age using 3D MRI scans as predictors; a linear regression model for the same purpose using brain morph metric measurements obtained from MRI images as predictors [12][13]. Both investigations linked brain ageing to a specific location on chromosome 17, and they discovered a mean age adjustment error (MAE) of around 3.5 years between PBA and actual chronological age [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Exploring Deep Relationship Learning for Regression: A Case Study on Brain Age Estimation

Mohamed shoaib,

Srithar

2024

Int Res J Adv Engg Hub

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To understand how the brain develops and matures, an accurate age estimate utilizing MRI pictures is essential. There has been encouraging progress in this area using deep learning-based methods, especially CNNs, which can extract important features from MRI scans. By combining the VGG16 and EDCNN networks, this study presents a new method for determining brain age. For feature extraction, we use VGG16, and for brain age prediction, we use EDCNN. The proposed method was evaluated using the Kaggle repository, a freely available benchmark dataset that includes magnetic resonance imaging (MRI) images of 562 individuals ranging in age from 20 to 86. Following the preprocessing of the dataset, the images were adjusted to 224 × 224 x 3 dimensions and normalized. Using a broad range of evaluation criteria, the proposed methodology's performance was contrasted with that of several state-of-the-art techniques. Additionally, ablation experiments were conducted as part of this study to determine the significance of various components of the suggested technique. The results showed that the method's performance was significantly improved when VGG16 was used as a feature extractor. In addition, this study found that the proposed method's performance was significantly improved after VGG16 network tuning. The research demonstrates that technologies based on deep learning might help us comprehend how the brain develops and ages. The approach uses EDCNN to forecast brain ageing and VGG16 to extract features. In order to decipher the structural subtleties seen in brain MRI images, the acclaimed feature extraction tool VGG16 is used as a base.

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Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques

Cited by 29 publications

References 11 publications

Machine Learning Based Stroke Predictor Application

Machine Learning Based Stroke Predictor Application

Feature Fusion-based Brain Stroke Identification Model Using Computed Tomography Images

Exploring Deep Relationship Learning for Regression: A Case Study on Brain Age Estimation

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