一种基于CNN深度学习模型的脑肿瘤检测方法

P. Pardhi, Navya Verma, Nikunj Loya, Kartik Agrawal
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引用次数: 0

摘要

肿瘤是由持续生长的异常细胞聚集产生的大量组织,而大脑是人体中最重要的器官,负责控制和调节人体所有重要的生命活动。脑瘤要么在大脑中形成,要么已经转移。然而,目前还没有发现导致脑瘤的原因。虽然脑肿瘤并不常见(约占所有癌症报告的1.8%),但恶性脑肿瘤的死亡风险特别高,因为肿瘤位于人体最重要的器官。为了降低死亡率,在早期阶段准确发现脑肿瘤是至关重要的。因此,我们提出了一种计算机辅助放射学方法,通过MRI扫描来评估脑肿瘤,用于脑肿瘤诊断管理。在这篇研究论文中,我们开发了一个模型,该模型使用分水岭技术对图像进行分割,提取特征,然后使用深度学习来高精度地检测癌症。
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A Novel Approach to Detect Brain Tumor Using CNN model of Deep Learning
A tumor is a mass of tissue generated by the aggregation of aberrant cells that continue to grow, and the brain is the most essential organ in the human body, responsible for controlling and regulating all critical life activities for the body. A brain tumor is either formed in the brain or has migrated. Yet, no reason has been found for developing brain tumors. Though brain tumors are uncommon (approximately 1.8 percent of all reported cancers), the death risk of malignant brain tumors is particularly high due to the tumor’s location in the body’s most essential organ. To reduce the mortality rate, it is critical to accurately detect brain tumors at an early stage. As a result, we’ve proposed a computer-assisted radiology method for assessing brain tumors from MRI scans forbrain tumor diagnostic management. In this research paper, we developed a model that uses the Watershed technique to segment images, extract features, and then use deep learning to detect cancers with high accuracy. 
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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