Citation

Hussain, Sumaira and Jan, Salman and Aldhayan, Manal and Khan, Mohammad Asmat Ullah and Kamal, Shahid and Jameel, Abid and Alkhateeb, Jawad Hasan and Zraqou, Jamal (2026) Lightweight deep learning framework for intracranial hemorrhage detection in brain CT scans. Frontiers in Medicine, 13. ISSN 2296-858X

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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 realworld deployment.

Item Type:	Article
Uncontrolled Keywords:	Automated diagnosis, brain CT, deep learning
Subjects:	R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 04:40
Last Modified:	04 Aug 2026 04:40
URII:	http://shdl.mmu.edu.my/id/eprint/16487

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