AI-Assisted Diagnosis of Cerebral Oedema Using Convolutional Neural Networks
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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.
KeywordsConvolutional neural network Colour gamut Cerebral Oedema CT scan Greyscale images Tensor flow
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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