Abstract The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.

中文翻译：

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使用手工制作和深度学习特征的融合进行脑肿瘤检测

摘要 当今世界范围内的危险疾病是脑肿瘤。肿瘤通过破坏健康组织或增加颅内压来影响大脑。因此，肿瘤细胞的快速生长可能导致死亡。因此，早期脑肿瘤诊断是一项更重要的任务，可以使患者免受不良影响。在所提出的工作中，应用 Grab cut 方法对实际病变症状进行准确分割，同时对迁移学习模型视觉几何组 (VGG-19) 进行微调以获取特征，然后将这些特征与手工制作的（形状和纹理）连接起来特征通过基于串行的方法。这些特征通过熵进行优化以实现准确和快速的分类，并将融合向量提供给分类器。所提出的模型分别在包括多模态脑肿瘤分割 (BRATS) 2015、2016 和 2017 在内的顶级医学图像计算和计算机辅助干预 (MICCAI) 挑战数据库上进行了测试。骰子相似系数（DSC）的测试结果分别在 BRATS 2015 上达到 0.99，在 BRATS 2015 上达到 1.00，在 BRATS 2017 上达到 0.99。