Conference Paper2024Cited by 2

Comparative Analysis of Deep Learning Models for Intracerebral Hemorrhage Detection from Computed Tomography

Taiseer Rakiin Ahad, Saadia Binte Alam, Farhan Ha-Mim, Ashad Nur, Sefatul Wasi, Rashedur Rahman

2024 27th International Conference on Computer and Information Technology (ICCIT)

IEEE, pp. 328-333

DOI: 10.1109/iccit64611.2024.11022361URL

Abstract

Intracerebral hemorrhage (ICH) is a serious medical condition caused by bleeding within the brain tissue, which can lead to life-threatening complications if not diagnosed quickly. This study compares the performance of three advanced deep learning models: InceptionV3, DenseNet201 and EfficientNetB4 for detecting ICH from computed tomography (CT) scans. The novelty of this work lies in the use of preprocessing techniques such as windowing with Hounsfield Units and Skull segmentation to provide cleaner input data, along with Grad-CAM visualizations to ensure the model’s predictions are interpretable. Using comprehensive evaluations, including metrics like AUROC, precision and F1 score, EfficientNetB4 achieved the best results with an accuracy of 0.85, precision of 0.87 and F1-score of 0.86, demonstrating its potential to assist medical professionals in making faster and more reliable diagnoses.

Keywords

  • intracerebral hemorrhage
  • CT image classification
  • deep learning model comparison
  • EfficientNet-B4
  • InceptionV3
  • DenseNet201
  • skull segmentation
  • Grad-CAM
  • model interpretability
  • automated diagnosis

CCDS Authors

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

  • Associate Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Md Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

  • Assistant Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

References

  1. 1.Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2016). Deep Residual Learning for Image Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, 770–778[10.1109/cvpr.2016.90]
  2. 2.Gao Huang, Zhuang Liu, Laurens Van Der Maaten, Kilian Q. Weinberger. (2017). Densely Connected Convolutional Networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, 2261–2269[10.1109/cvpr.2017.243]
  3. 3.Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, 4510–4520[10.1109/cvpr.2018.00474]
  4. 4.J. Claude Hemphill, Steven M. Greenberg, Craig S. Anderson, Kyra Becker, Bernard R. Bendok, Mary Cushman, Gordon L. Fung, Joshua N. Goldstein, R. Loch Macdonald, Pamela H. Mitchell, Phillip A. Scott, Magdy H. Selim, Daniel Woo. (2015). Guidelines for the Management of Spontaneous Intracerebral Hemorrhage. Stroke, 46(7), 2032–2060[10.1161/str.0000000000000069]
  5. 5.Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz, Hugo J. W. L. Aerts. (2018). Artificial intelligence in radiology. Nature reviews. Cancer, 18(8), 500–510[10.1038/s41568-018-0016-5]
  6. 6.Leslie N. Smith. (2017). Cyclical Learning Rates for Training Neural Networks. IEEE Winter Conference on Applications of Computer Vision, 464–472[10.1109/wacv.2017.58]
  7. 7.Satoshi Suzuki, KeiichiA be. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision Graphics and Image Processing, 30(1), 32–46[10.1016/0734-189x(85)90016-7]
  8. 8.Foster Provost, Tom Fawcett. (2001). Robust Classification for Imprecise Environments. Machine Learning, 42(3), 203–231[10.1023/a:1007601015854]
  9. 9.Sang Joon An, Tae Jung Kim, Byung-Woo Yoon. (2017). Epidemiology, Risk Factors, and Clinical Features of Intracerebral Hemorrhage: An Update. Journal of Stroke, 19(1), 3–10[10.5853/jos.2016.00864]
  10. 10.Joyce S Balami, Alastair M Buchan. (2011). Complications of intracerebral haemorrhage. The Lancet Neurology, 11(1), 101–118[10.1016/s1474-4422(11)70264-2]
  11. 11.Hyunkwang Lee, Sehyo Yune, Mohammad Mansouri, Myeongchan Kim, Shahein H. Tajmir, Claude E. Guerrier, Sarah A. Ebert, Stuart R. Pomerantz, Javier M. Romero, Shahmir Kamalian, Ramon G. Gonzalez, Michael H. Lev, Synho Do. (2018). An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nature Biomedical Engineering, 3(3), 173–182[10.1038/s41551-018-0324-9]
  12. 12.J. Alfredo Caceres, Joshua N. Goldstein. (2012). Intracranial Hemorrhage. Emergency Medicine Clinics of North America, 30(3), 771–794[10.1016/j.emc.2012.06.003]
  13. 13.Hai Ye, Feng Gao, Youbing Yin, Danfeng Guo, Pengfei Zhao, Yi Lu, Xin Wang, Junjie Bai, Kunlin Cao, Qi Song, Heye Zhang, Wei Chen, Xuejun Guo, Jun Xia. (2019). Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. European Radiology, 29(11), 6191–6201[10.1007/s00330-019-06163-2]
  14. 14.Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar, Srikrishna Varadarajan. (2018). RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. IEEE International Symposium on Biomedical Imaging (ISBI), 281–284[10.1109/isbi.2018.8363574]
  15. 15.Richard J. Lederman. (2012). Bradley’s Neurology in Clinical Practice. JAMA, 308(16), 1694[10.1001/jama.308.16.1694-a]
  16. 16.Daniel Woo, Mary E. Comeau, Simone Uniken Venema, Christopher D. Anderson, Matthew Flaherty, Fernando Testai, Steven Kittner, Michael Frankel, Michael L. James, Gene Sung, Mitchell Elkind, Bradford Worrall, Chelsea Kidwell, Nicole Gonzales, Sebastian Koch, Christiana Hall, Lee Birnbaum, Douglas Mayson, Bruce Coull, Marc Malkoff, Kevin N. Sheth, Jacob L. McCauley, Jennifer Osborne, Misty Morgan, Lee Gilkerson, Tyler Behymer, Elisheva R. Coleman, Jonathan Rosand, Padmini Sekar, Charles J. Moomaw, Carl D. Langefeld. (2022). Risk Factors Associated With Mortality and Neurologic Disability After Intracerebral Hemorrhage in a Racially and Ethnically Diverse Cohort. JAMA Network Open, 5(3), e221103[10.1001/jamanetworkopen.2022.1103]
  17. 17.Yuan Yang, Lin Zhang, Mingyu Du, Jingyu Bo, Haolei Liu, Lei Ren, Xiaohe Li, M. Jamal Deen. (2021). A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions. Computers in Biology and Medicine, 139, 104887[10.1016/j.compbiomed.2021.104887]
  18. 18.Aniwat Phaphuangwittayakul, Yi Guo, Fangli Ying, Ahmad Yahya Dawod, Salita Angkurawaranon, Chaisiri Angkurawaranon. (2021). An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury. Applied Intelligence, 52(7), 7320–7338[10.1007/s10489-021-02782-9]
  19. 19.Yong-Hong Li, Liang Zhang, Qing-Mao Hu, Hong-Wei Li, Fu-Cang Jia, Jian-Huang Wu. (2011). Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans. International Journal of Computer Assisted Radiology and Surgery, 7(4), 507–516[10.1007/s11548-011-0664-3]
  20. 20.Kingma, Diederik P., Ba, Jimmy. (2014). Adam: A Method for Stochastic Optimization. arXiv (Cornell University)[10.48550/arxiv.1412.6980]
  21. 21.Tan, Mingxing, Le, Quoc V.. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv (Cornell University)[10.48550/arxiv.1905.11946]
  22. 22.Chilamkurthy, Sasank, Ghosh, Rohit, Tanamala, Swetha, Biviji, Mustafa, Campeau, Norbert G., Venugopal, Vasantha Kumar, Mahajan, Vidur, Rao, Pooja, Warier, Prashant. (2018). Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans. arXiv (Cornell University)[10.48550/arxiv.1803.05854]

Cited By

  1. 1.Sauharda Sarkar, Farha Yesmin, Md. Abrar Sayed Tasin, Fatin Israq, Siam Tahsin Bhuiyan, Rashedur Rahman, Saadia Binte Alam, Ashraful Islam. (2025). Effect of Augmentations on CNN Based Models for Congenital Heart Disease Detection from Chest X-Rays. IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 438–443[10.1109/wiecon-ece69386.2025.11526447]
  2. 2.Abdullah Al Alam Shanto, Mumtahina Esha, Sadia Alam Kotha, Riyadul Islam, Sefatul Wasi, Siam Tahsin Bhuiyan, Rashedur Rahman, Saadia Binte Alam. (2025). Assessing the Impact of Architectural Design in Deep Learning Models for Fundus-Based Ocular Disease Detection. IEEE International Conference on Computing, Applications and Systems (COMPAS), 1–6[10.1109/compas67506.2025.11381612]
← All publications