Advertisement

AI-Assisted Diagnosis of Cerebral Oedema Using Convolutional Neural Networks

  • B. Sri Gurubaran
  • Takamichi Hirata
  • A. Umamakeswari
  • E. R. S. Subramanian
  • A. S. Sayee Shruthi
Chapter
  • 26 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 103)

Abstract

With the current advances in medical sciences, it is easy to observe the changes happening in the brain in real-time. But the procedure involved is costly and invasive in nature. So, the commonly used procedure is to obtain computed tomography (CT) scans of the brain, which provides static greyscale images. The biggest drawback of a CT scan is that the images are in greyscale; therefore, it is difficult for the naked eye to distinguish the subtle changes in the brain tissues. A wrong prognosis, in this case, could lead to the death of a patient. In this paper, we propose an AI-assisted diagnosis method where a predictive model is deployed, which can discern even the subtlest of the differences in the brain tissues and can help determine any anomalies. The model was trained and tested using CT scans of a rat’s brain, which is affected by Cerebral Oedema (a certain type of disease which leads to accumulation of fluid in the intracellular or the extracellular spaces of the brain). To improve the accuracy of the model, a colour gamut transformation is also proposed. The results after testing the model, with and without the transformation, are tabulated.

Keywords

Convolutional neural network Colour gamut Cerebral Oedema CT scan Greyscale images Tensor flow 

Notes

Acknowledgements

We are grateful to the Institute of Development, Ageing and Cancer, of Tohoku University for providing access to their Rat Brain Image Database, which greatly helped us with our research and improved the results obtained in this manuscript.

References

  1. 1.
    Kononenko I, Kukar M (1995) Machine learning for medical diagnosis. In: Proceedings of the CADAMGoogle Scholar
  2. 2.
    Kharrat A, Gasmi K, Messaoud MB, Benamrane N, Abid M (2010) A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo J Sci 17(1):71–82Google Scholar
  3. 3.
    Peng X, Lin P, Zhang T, Wang J (2013) Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS ONE 8(11):e79476CrossRefGoogle Scholar
  4. 4.
    Salvatore C et al (2014) Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and Progressive Supranuclear Palsy. J Neurosci Methods 222:230–237CrossRefGoogle Scholar
  5. 5.
    Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62(6):1609–1618CrossRefGoogle Scholar
  6. 6.
    Zacharaki EI, Kanas VG, Davatzikos C (2011) Investigating machine learning techniques for MRI-based classification of brain neoplasms. Int J Comput Assist Radiol Surg 6(6):821–828CrossRefGoogle Scholar
  7. 7.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  8. 8.
    Smith AR (1978) Color gamut transform pairs. ACM Siggraph Comput Graph 12(3):12–19CrossRefGoogle Scholar
  9. 9.
    Guyon I (1997) A scaling law for the validation-set training-set size ratio. AT&T Bell Lab 1–11Google Scholar
  10. 10.
    Abadi M et al (2016) TensorFlow: a system for large-scale machine learning. OSDI 16:265–283Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • B. Sri Gurubaran
    • 1
  • Takamichi Hirata
    • 2
  • A. Umamakeswari
    • 1
  • E. R. S. Subramanian
    • 1
  • A. S. Sayee Shruthi
    • 1
  1. 1.CSE, SOCSASTRA UniversityThanjavurIndia
  2. 2.Department of Biomedical EngineeringTokyo City UniversityTokyoJapan

Personalised recommendations