What question did this study set out to answer?

The central aim is to develop a deep learning model for accurate detection of brain strokes using CT scans.

March 28, 2026Open Access

A Deep Learning Model for Brain Stroke Detection Using Ct Scans

Puntos clave

The central aim is to develop a deep learning model for accurate detection of brain strokes using CT scans.
Utilized Convolutional Neural Networks (CNN) for feature extraction from CT images.
Employed Long Short-Term Memory (LSTM) models to enhance classification accuracy.
Analyzed CT scans to detect brain strokes and evaluate model performance.
Achieved high accuracy in detecting brain strokes from CT scans.
Demonstrated reliability of the deep learning model in diagnosing strokes.
Showed potential to improve clinical decision-making in stroke detection.

Resumen

—Brain stroke is a serious neurological condition that requires quick and accurate diagnosis to reduce the risk of death and long-term disability. Computed Tomography (CT) scans are commonly used for stroke detection because they are fast and easily available. However, analyzing these images manually can take time and may vary between radiologists. This study presents a deep learning–based system for detecting brain stroke using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. The CNN model helps in extracting important features from CT images, while the LSTM model enhances the classification process. The results show high accuracy and reliability, highlighting the potential of deep learning in supporting medical image analysis and improving clinical decision-making.

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