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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

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Related Experiment Video

Updated: Jun 20, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Towards trustworthy brain stroke diagnosis using a lightweight explainable deep learning framework for CT imaging.

Md Romzan Alom1, Muhammad Aminur Rahaman2, Md Parvez Hossain3

  • 1Department of CSE, Bangladesh University of Business and Technology (BUBT), Rupnagar, Mirpur-2, 1216, Dhaka, Bangladesh.

Scientific Reports
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight deep learning system for rapid, automated brain stroke detection from CT scans. The Deep Neural Brain Stroke Detection (DNBSD) system offers high accuracy and interpretability, aiming to improve clinical workflows.

Keywords:
Clinical decision support systemComputed tomography imagingConvolutional neural networksDeep learningDeep neural brain stroke detectionExplainable AI.Medical imaging

Related Experiment Videos

Last Updated: Jun 20, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Manual interpretation of CT scans for brain stroke diagnosis is time-consuming and can delay critical clinical decisions.
  • Automated detection systems are needed to expedite diagnosis in time-sensitive stroke cases.

Purpose of the Study:

  • To develop and evaluate a lightweight deep learning framework, the Deep Neural Brain Stroke Detection (DNBSD) system, for automated brain stroke detection from CT images.
  • To assess the performance and interpretability of the DNBSD system in resource-constrained clinical settings.

Main Methods:

  • A convolutional neural network (CNN) architecture with optimized parameters (1.67 million trainable parameters, 0.2973 GFLOPs) was designed for stroke detection.
  • Image resizing and normalization were used for preprocessing, and the model was trained on two public datasets (BSCI, BSPCSI).
  • Explainable AI techniques (LIME, Grad-CAM) and a web-based diagnostic tool were integrated for enhanced interpretability and real-time prediction.

Main Results:

  • The DNBSD system achieved high accuracy and AUC on both BSCI and BSPCSI datasets, outperforming baseline and state-of-the-art deep learning models.
  • The lightweight design makes the system suitable for deployment in environments with limited computational resources.
  • Integrated explainable AI techniques successfully highlighted critical regions influencing stroke detection predictions.

Conclusions:

  • The proposed Deep Neural Brain Stroke Detection (DNBSD) system is an effective and interpretable tool for automated stroke detection from CT images.
  • The system has the potential to significantly enhance clinical diagnostic workflows by providing rapid and accurate stroke detection.
  • The development of a web-based tool further supports real-time application and clinical decision-making.