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Abstract：
UNASSIGNED: Brain cancer is a frequently occurring disease around the globe and mostly developed due to the presence of tumors in/around the brain. Generally, the prevalence and incidence of brain cancer are much lower than that of other cancer types (breast, skin, lung, etc.). However, brain cancers are associated with high mortality rates, especially in adults, due to the false identification of tumor types, and delay in the diagnosis. Therefore, the minimization of false detection of brain tumor types and early diagnosis plays a crucial role in the improvement of patient survival rate. To achieve this, many researchers have recently developed deep learning (DL)-based approaches since they showed a remarkable performance, particularly in the classification task.
UNASSIGNED: This article proposes a novel DL architecture named BrainCDNet. This model was made by concatenating the pooling layers and dealing with the overfitting issues by initializing the weights into layers using \'He Normal\' initialization along with the batch norm and global average pooling (GAP). Initially, we sharpen the input images using a Nimble filter, which results in maintaining the edges and fine details. After that, we employed the suggested BrainCDNet for the extraction of relevant features and classification. In this work, two different forms of magnetic resonance imaging (MRI) databases such as binary (healthy vs. pathological) and multiclass (glioma vs. meningioma vs. pituitary) are utilized to perform all these experiments.
UNASSIGNED: Empirical evidence suggests that the presented model attained a significant accuracy on both datasets compared to the state-of-the-art approaches, with 99.45% (binary) and 96.78% (multiclass), respectively. Hence, the proposed model can be used as a decision-supportive tool for radiologists during the diagnosis of brain cancer patients.
UNASSIGNED: This article proposes a novel DL architecture named BrainCDNet. This model was made by concatenating the pooling layers and dealing with the overfitting issues by initializing the weights into layers using \'He Normal\' initialization along with the batch norm and global average pooling (GAP). Initially, we sharpen the input images using a Nimble filter, which results in maintaining the edges and fine details. After that, we employed the suggested BrainCDNet for the extraction of relevant features and classification. In this work, two different forms of magnetic resonance imaging (MRI) databases such as binary (healthy vs. pathological) and multiclass (glioma vs. meningioma vs. pituitary) are utilized to perform all these experiments.
UNASSIGNED: Empirical evidence suggests that the presented model attained a significant accuracy on both datasets compared to the state-of-the-art approaches, with 99.45% (binary) and 96.78% (multiclass), respectively. Hence, the proposed model can be used as a decision-supportive tool for radiologists during the diagnosis of brain cancer patients.
摘要：
脑癌是全球经常发生的疾病，主要是由于大脑中/周围存在肿瘤而发展起来的。一般来说,脑癌的患病率和发病率远低于其他癌症类型(乳腺癌，皮肤,肺，等。).然而,脑癌与高死亡率相关，尤其是成年人，由于对肿瘤类型的错误识别，延迟诊断。因此,减少对脑肿瘤类型的错误检测和早期诊断对提高患者生存率起着至关重要的作用。为了实现这一点，许多研究人员最近开发了基于深度学习(DL)的方法，因为它们表现出了显著的性能，特别是在分类任务中。
■本文提出了一种名为BrainCDNet的新型DL架构。该模型是通过连接池化层并通过使用“HeNormal”初始化以及批量范数和全局平均池化（GAP）将权重初始化到层中来处理过拟合问题的。最初，我们使用灵活的过滤器锐化输入图像，这导致保持边缘和精细的细节。之后,我们使用建议的BrainCDNet来提取相关特征和分类。在这项工作中,两种不同形式的磁共振成像(MRI)数据库，如二进制(健康与病理性)和多类(神经胶质瘤vs.脑膜瘤vs.垂体）用于执行所有这些实验。
■经验证据表明，与最先进的方法相比，所提出的模型在两个数据集上都获得了显着的准确性，99.45%(二进制)和96.78%(多类)，分别。因此，所提出的模型可作为放射科医师在脑癌患者诊断过程中的决策支持工具.
■本文提出了一种名为BrainCDNet的新型DL架构。该模型是通过连接池化层并通过使用“HeNormal”初始化以及批量范数和全局平均池化（GAP）将权重初始化到层中来处理过拟合问题的。最初，我们使用灵活的过滤器锐化输入图像，这导致保持边缘和精细的细节。之后,我们使用建议的BrainCDNet来提取相关特征和分类。在这项工作中,两种不同形式的磁共振成像(MRI)数据库，如二进制(健康与病理性)和多类(神经胶质瘤vs.脑膜瘤vs.垂体）用于执行所有这些实验。
■经验证据表明，与最先进的方法相比，所提出的模型在两个数据集上都获得了显着的准确性，99.45%(二进制)和96.78%(多类)，分别。因此，所提出的模型可作为放射科医师在脑癌患者诊断过程中的决策支持工具.
