Abstract
Patients having an injury are susceptible to internal bleeding in the brain which is very dangerous. Computer-aided diagnosis systems (CAD), as their name suggests, deploy computers to assist doctors in making quick and faultless diagnosis. CAD systems assess a variety of inputs, including clinical findings, symptoms, and medical images. Medical image-based diagnosis is one of the most popular methods of diagnosis. Our goal is to find a solution to the problem of brain hemorrhages, specifically when the symptoms are severe, such as headaches or unconsciousness. Using deep convolutional neural networks, we intend to create a model that not only does the basic analysis and determine whether a cerebral hemorrhage is present or not in CT scan images of the brain but also detect the kind of the hemorrhage. Image pre-processing, feature extraction, image segmentation, and classification are all stages in the process of detecting and recognizing hemorrhage. We are also doing a comparative study on algorithms in detecting cerebral hemorrhage on data containing CT scan images. By doing this, we are trying to find the algorithm which gives best performance.
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Bharath Kumar Chowdary, P., Jahnavi, P., Rani, S.S., Chowdary, T.J., Srija, K. (2022). Detection and Classification of Cerebral Hemorrhage Using Neural Networks. In: Reddy, A.B., Kiranmayee, B., Mukkamala, R.R., Srujan Raju, K. (eds) Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7389-4_54
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DOI: https://doi.org/10.1007/978-981-16-7389-4_54
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