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.
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References
Kononenko I, Kukar M (1995) Machine learning for medical diagnosis. In: Proceedings of the CADAM
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):71–82
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):e79476
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 222:230–237
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):1609–1618
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):821–828
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Smith AR (1978) Color gamut transform pairs. ACM Siggraph Comput Graph 12(3):12–19
Guyon I (1997) A scaling law for the validation-set training-set size ratio. AT&T Bell Lab 1–11
Abadi M et al (2016) TensorFlow: a system for large-scale machine learning. OSDI 16:265–283
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.
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Sri Gurubaran, B., Hirata, T., Umamakeswari, A., Subramanian, E.R.S., Sayee Shruthi, A.S. (2020). AI-Assisted Diagnosis of Cerebral Oedema Using Convolutional Neural Networks. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_44
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DOI: https://doi.org/10.1007/978-981-15-2043-3_44
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