The public health is significantly affected by development of brain tumors in human patients. Glioblastoma (GBM) is a relatively common, malignant form of brain tumor, which is currently challenging to treat and cure. In contrast, Lower Grade Gliomas (LGGs) originate from glial cells and can mostly be treated and cured in the initial stages if they are detected early. A computer-aided diagnostics (CAD) tool may help to test for the presence and extent of any such tumor, and thus can be assistive in the clinical diagnostic process. Herein, we compare handcrafted versus non-handcrafted features-based CAD to characterize GBM and LGG. Our machine learning-based handcrafted model uses quantitative techniques of enhanced elongated quinary patterns and entropies analysis. We have also developed a non-handcrafted deep learning model using Visual Geometry Group-16 architecture for segregating GBM and LGG subjects results in 94.25% accuracy using k-nearest neighbor classifier.

中文翻译：

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使用磁共振图像自动检测脑肿瘤的特征与基于深度学习的方法：一项比较研究

人类患者脑瘤的发展显着影响公共健康。胶质母细胞瘤 (GBM) 是一种相对常见的恶性脑肿瘤，目前治疗和治愈具有挑战性。相比之下，低级别胶质瘤 (LGG) 起源于胶质细胞，如果早期发现，大部分可以在初始阶段进行治疗和治愈。计算机辅助诊断 (CAD) 工具可以帮助测试任何此类肿瘤的存在和范围，因此可以在临床诊断过程中提供帮助。在这里，我们比较了手工制作和非手工制作的基于特征的 CAD 来表征 GBM 和 LGG。我们基于机器学习的手工模型使用增强的细长五元模式和熵分析的定量技术。