ArticleBrain sciences2023
An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- A Resource-Efficient Hybrid Deep Learning Framework with External Validation for Intracranial Hemorrhage Detection in CT Scans.Diagnostics (Basel, Switzerland) · 2026Article
- The use of artificial intelligence based chat bots in ophthalmology triage.Eye (London, England) · 2025Article
- The association between hemoglobin-to-red blood cell distribution width ratio and 28-day mortality in epidural hemorrhage: a cohort study.Frontiers in neurology · 2025Article
- Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.PloS one · 2025Article
- HU to RGB transformation with automatic windows selection for intracranial hemorrhage classification using ncCT.PloS one · 2025Article
- Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan.Brain sciences · 2023Article
- Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient's skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms.
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