Abstract
A brain haemorrhage is a form of stroke that occurs when a blood vessel in the brain bursts, producing bleeding in the surrounding tissues. The accurate assessment of malady and the excavation of robust and reliable measurements for sick people to define the morphological brain changes as the recovery developments are made possible by the ability to diagnose brain haemorrhage, which is primarily done through the investigation of a CT scan. Even though much research has indeed been accomplished on medical image segmentation, there’s still some potential for more studies in the context of brain haemorrhage prognosis considering the low accuracy of prevailing techniques and algorithms, programming uncertainty of developed approaches, preposterousness in the real world, and a dearth of many other advancements that could make the process quite impactful and beneficial. Furthermore, many of the current techniques only target the identification of a small number of brain haemorrhage subtypes. In this paper, we are focusing on the application of convolution neural networks, which is a deep learning technique to detect brain haemorrhage, and we found that the classification accuracy of the model is 89.9%.
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Navadia, N.R., Kaur, G., Bhardwaj, H. (2023). Brain Hemorrhage Detection Using Deep Learning: Convolutional Neural Network. In: Garg, L., et al. Information Systems and Management Science. ISMS 2021. Lecture Notes in Networks and Systems, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-031-13150-9_46
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DOI: https://doi.org/10.1007/978-3-031-13150-9_46
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