Lightweight deep learning framework for intracranial hemorrhage detection in brain CT scans.

Overview

1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
2. Faculty of Computer Studies, Arab Open University-Bahrain, A’ali, Bahrain
3. Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
4. Department of Computer Science, College of Computer and IT, Shaqra University, Shaqra, Saudi Arabia
5. Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyad, Saudi Arabia
6. Department of Computer Science, Faculty of Computing, International Islamic University Islamabad, Islamabad, Pakistan
7. Computer Engineering Department, College of Engineering and Computer Science, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
8. Department of Computer Science, University of Petra, Amman, Jordan

Abstract

Intracranial hemorrhage, including clinically significant intracranial hemorrhage conditions, is a life-threatening condition in which rapid and accurate detection through brain computed tomography (CT) scans is crucial for patient survival. Manual interpretation of these scans remains time-consuming and may vary between observers, leading to potential diagnostic delays. This study investigates the application of lightweight and mobile-optimized deep-learning models for the automated detection of intracranial hemorrhage using a curated subset of a publicly available brain CT hemorrhage dataset, for automated intracranial hemorrhage detection. A custom convolutional neural network (CNN) and two transfer-learning architectures MobileNetV2 and EfficientNet-B0 were evaluated in terms of diagnostic accuracy, generalization, and computational efficiency. Among the tested models, MobileNetV2 demonstrated the highest overall performance, achieving an accuracy of 87% and an AUC of 0.94, while the lightweight CNN achieved 79% accuracy. EfficientNet-B0 also showed competitive results but required greater computational resources. The findings demonstrate that lightweight neural architectures can achieve reliable diagnostic performance while remaining suitable for assistive decision-support applications, although further improvement in sensitivity and clinical validation are required. The study highlights that carefully optimized deep-learning systems can support preliminary clinical assessment; however, additional validation and performance refinement are necessary before practical real-world deployment.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/abdulkader90, at Kaggle; found in the references

Data availability statement

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 8 keywords, 1 funder, 14 references.

Cite

This paper

Hussain, S., Jan, S., Aldhayan, M., Khan, M. A. U., Kamal, S., Jameel, A., Alkhateeb, J. H., & Zraqou, J. (2026). Lightweight deep learning framework for intracranial hemorrhage detection in brain CT scans. Frontiers in medicine, 13, 1804084. https://doi.org/10.3389/fmed.2026.1804084

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