ArticleComputational and mathematical methods in medicine2022
Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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9 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- A randomized multi-window 3D deep learning approach for intracranial hemorrhage detection on non-contrast head CT.Medical & biological engineering & computing · 2026Article
- Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage.World journal of radiology · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- An efficient deep learning network for brain stroke detection using salp shuffled shepherded optimization.Scientific reports · 2025Article
- Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Diagnostic performance and clinical applications of artificial intelligence for intracranial bleeding detection: A meta-analysis.Brain & spine · 2025Article
- Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.European radiology · 2024Article
- Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network.Frontiers in neurology · 2024Article
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which occurs due to ruptures of weakened blood vessel in brain tissue. It is a serious medical emergency issues that needs immediate treatment. Large numbers of noncontrast-computed tomography (NCCT) brain images are analyzed manually by radiologists to diagnose the hemorrhagic stroke, which is a difficult and time-consuming process. In this study, we propose an automated transfer deep learning method that combines ResNet-50 and dense layer for accurate prediction of intracranial hemorrhage on NCCT brain images. A total of 1164 NCCT brain images were collected from 62 patients with hemorrhagic stroke from Kalinga Institute of Medical Science, Bhubaneswar and used for evaluating the model. The proposed model takes individual CT images as input and classifies them as hemorrhagic or normal. This deep transfer learning approach reached 99.6% accuracy, 99.7% specificity, and 99.4% sensitivity which are better results than that of ResNet-50 only. It is evident that the deep transfer learning model has advantages for automatic diagnosis of hemorrhagic stroke and has the potential to be used as a clinical decision support tool to assist radiologists in stroke diagnosis.
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