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Convolution Neural Network Based Enhanced Computerized Technique for Brain Tumour Detection

机译:基于卷积神经网络的脑肿瘤检测增强计算机化技术

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Recent years have witnessed how medical imaging is growing these days. Brain Tumour is considered as the world's 10th largest fatal human disease. In this perspective, it is a challenging task for the neurosurgeons to reduce the death rates that occur due to brain tumour. For reducing the death rates, it is the primary duty to identify the tumour cells in their early stage. It is very difficult for the tumour cells to be identified with naked eye for by looking to the MR images. In this paper, an automatic cancer cell detection system is proposed to minimize the burden of the neurosurgeons to identify the cancer cells. Here, CNN (Convolution Neural Network) based system with Median filtering and texture feature extraction is proposed and it gives results with 93 % accuracy and at present it has been proved as the better model for brain tumour cells detection.
机译:近年来目睹了这些天医学成像如何增长。 脑肿瘤被认为是世界的10 th 最大的致命人类疾病。 在这种观点中,神经外科医生是一个具有挑战性的任务,以减少由于脑肿瘤引起的死亡率。 为了减少死亡率,它是在早期识别肿瘤细胞的主要职责。 肿瘤细胞通过向MR图像呈现肉眼鉴定肿瘤细胞非常困难。 在本文中,提出了一种自动癌细胞检测系统,以最小化神经外部的负担来鉴定癌细胞。 这里,提出了基于CNN(卷积神经网络)具有中值滤波和纹理特征提取的系统,并提供了93%的精度,并且目前已被证明是脑肿瘤细胞检测的更好模型。

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