Abstract
Brain hemorrhage is a life-threatening problem that happens by bleeding inside human head. In this study, computed tomography (CT) scan images have been used to classify whether the case is hemorrhage or non-hemorrhage. Different convolutional neural network (CNN) models have been observed along with some pre-trained deep learning models such as VGG16, VGG19, ResNet150, ResNet152 and InceptionV3. Pre-trained models have performed well on the dataset but all of them are heavyweight architectures in terms of number of total parameters. But the proposed model is a lightweight architecture as well as a well performing one. After evaluating the model performance, it has been observed that the proposed model gave 96.67% accuracy, 97.08% sensitivity and 96.25% specificity which is the best among other custom CNN models.
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References
Heit JJ, Iv M, Wintermark M (2017) Imaging of intracranial hemorrhage. J Stroke 19(1):11
Mohammed A et al (2022) Deep learning models for intracranial hemorrhage recognition: a comparative study. Procedia Comput Sci 196:418–425
Van Asch CJJ et al (2010) Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol 9(2):167–176
Kuohn LR et al (2020) Cause of death in spontaneous intracerebral hemorrhage survivors: multistate longitudinal study. Neurology 95(20):e2736–e2745
Haselsberger K, Pucher R, Auer LM (1988) Prognosis after acute subdural or epidural haemorrhage. Acta Neurochir 90(3):111–116
Napier J et al (2019) A CAD system for brain haemorrhage detection in head CT scans. In: IEEE EUROCON 2019-18th international conference on smart technologies. IEEE, pp 1–6
Srivastava DK, Sharma B, Singh A (2018) Classification of hematomas in brain CT images using support vector machine. In: Information and communication technology for sustainable development. Springer, Berlin, pp 375–385
Vrbancic G, Zorman M, Podgorelec V (2019) Transfer learning tuning utilizing grey wolf optimizer for identification of brain hemorrhage from head CT images. In: StuCoSReC: proceedings of the 2019 6th student computer science research conference, pp 61–66
Mushtaq MF et al (2021) BHCNet: neural network-based brain hemorrhage classification using head CT scan. IEEE Access 9:113901–113916
Ajay P et al (2019) Image level training and prediction: intracranial hemorrhage identification in 3D non-contrast CT. IEEE Access 7:92355–92364
Kitamura F (2022) Head CT—-hemorrhage. https://www.kaggle.com/felipekitamura/head-ct-hemorrhage. [Online; Accessed 21 Mar 2022]
Das S, Riaz Rahman Aranya OFM, Labiba NN (2019) Brain tumor classification using convolutional neural network. In: 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). IEEE, pp 1–5
Yakun C et al (2018) Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 6:11782–11792
Krishna ST, Kalluri HK (2019) Deep learning and transfer learning approaches for image classification. Int J Recent Technol Eng (IJRTE) 7(5S4):427–432
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
He K et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Szegedy C et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
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Anjum, N., Sakib, A.N.M., Masudul Ahsan, S.M. (2023). Classification of Brain Hemorrhage Using Deep Learning from CT Scan Images. In: Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-7528-8_15
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DOI: https://doi.org/10.1007/978-981-19-7528-8_15
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