Leveraging Classification of Brain Tumour using Deep Learning Architectures

Karunya K, Karpagam G.R, J Shreyas
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Abstract

Despite advances in human intellect and biomedical in the last few decades, people continue to suffer from various cancers due to their volatile nature. This disease is still a major issue for the entire humanity. Brain tumour is one of the most crucial and serious illnesses. Oftheentire primary central nervous system tumours, Brain tumorsmake up 85 to 90%. It isestimatedthat 18,600 adults, including 8,100 women and 10,500 men, will die of primary cancerous tumors of the brain and centralnervoussystem tumors this year. Among the children of various age groups also it is seen as one of the most crucial cancers. Thus, accurate and timely handling of this disease is decisive. In order to speed up the process of brain tumour detection (augmented with accuracy, reliability and experience)Deep learning models can be used.To efficiently diagnose brain tumour kinds and compare classification performance, the proposed work makes optimal use of a newly modelled Convolutional Neural Network and ResNet 50,a pre-trained network.
利用深度学习架构进行脑肿瘤分类
尽管在过去的几十年里,人类的智力和生物医学都取得了进步,但由于癌症的不稳定性,人们仍然遭受着各种癌症的折磨。这种疾病仍然是全人类面临的一个重大问题。脑瘤是最严重的疾病之一。在整个原发性中枢神经系统肿瘤中,脑肿瘤占85%至90%。据估计,今年将有18600名成年人(包括8100名女性和10500名男性)死于原发性脑癌和中枢神经系统肿瘤。在各个年龄段的儿童中,它也被视为最严重的癌症之一。因此,准确和及时地处理这种疾病是决定性的。为了加快脑肿瘤检测的过程(增强准确性、可靠性和经验),可以使用深度学习模型。为了有效地诊断脑肿瘤类型并比较分类性能,本文提出的工作优化使用了新建模的卷积神经网络和预训练网络ResNet 50。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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