Brain stroke detection from computed tomography images using deep learning algorithms

Di̇ker, Aykut; Elen, Abdullah; Subaşı, Abdülhamit

doi:10.1016/b978-0-443-18450-5.00013-x

Cited by 7 publications

(2 citation statements)

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“…Artificial intelligence (AI) algorithms are trained on extensive datasets of medical imaging scans, including computed tomography (CT) and magnetic resonance imaging (MRI) images, to identify patterns linked to stroke, such as ischemic regions or hemorrhages (Pereira et al, 2018). Radiologists and physicians may benefit from these algorithms' ability to automatically evaluate new imaging scans for possible stroke symptoms, leading to a faster and more precise diagnosis (Diker et al, 2023). Conventional approaches to manually examining medical photographs may be laborious and influenced by personal opinions.…”

Section: Introductionmentioning

confidence: 99%

“…AI algorithms embedded in electronic health records may examine patient information such as clinical notes, test findings, imaging reports, and prescription histories to provide decision-making assistance to healthcare professionals (Diker et al, 2023). The algorithms may discover critical clinical data, detect possible stroke instances, provide suitable diagnostic procedures or therapies, and notify doctors of relevant recommendations or protocols.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning-based Brain Stroke Prediction Model Using Magnetic Resonance Imaging

Abulfaraj,

Dutta,

Sait

2024

Journal of Disability Research

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Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. Earlier detection and intervention can reduce the impact of BS. Magnetic resonance imaging (MRI) is commonly applied for BS detection. Deep learning techniques can employ MRI images to identify the BS risks in the initial stages. This study developed a BS detection model using an ensemble learning approach that combines the predictions of the base models. A MobileNet V3 model backbone was used to extract the intricate patterns of BS from MRI images. LightGBM and CatBoost models were used as base models to predict BS using the extracted features. In addition, the random forest model was used to integrate the predictions of base models to identify BS. The proposed model was generalized on a public MRI dataset that covers 2888 clinical MRI images. The experimental outcomes showed the effectiveness of the suggested BS detection model. The proposed model has obtained an accuracy of 98.7%, an area under the receiver operating characteristic score of 0.95, and an area under the precision–recall curve of 0.92. The recommended model is believed to be deployed in real-time healthcare settings to assist radiologists and clinicians in making effective decisions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%