Lightweight deep learning framework for intracranial hemorrhage detection in brain CT scans.
Authors
Affiliations (8)
Affiliations (8)
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China.
- Faculty of Computer Studies, Arab Open University-Bahrain, A'ali, Bahrain.
- Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia.
- Department of Computer Science, College of Computer and IT, Shaqra University, Shaqra, Saudi Arabia.
- Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyad, Saudi Arabia.
- Department of Computer Science, Faculty of Computing, International Islamic University Islamabad, Islamabad, Pakistan.
- Computer Engineering Department, College of Engineering and Computer Science, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
- 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.