论文标题
使用磁共振成像进行脑肿瘤检测的深度学习方法
A deep learning approach for brain tumor detection using magnetic resonance imaging
论文作者
论文摘要
大脑组织中异常细胞的生长会导致脑肿瘤。脑肿瘤被认为是儿童和成人中最危险的疾病之一。它很快就会发展，如果不适当治疗，患者的生存前景也很小。适当的治疗计划和精确诊断对于改善患者的预期寿命至关重要。脑肿瘤主要使用磁共振成像（MRI）诊断。作为基于卷积神经网络（CNN）的插图的一部分，已经提出了一种结构，其中包含五个卷积层，五层最大层层，一个平坦的层和两个密集的层，用于从MRI图像中检测脑肿瘤。提出的模型包括自动特征提取器，修改的隐藏层体系结构和激活函数。进行了几个测试用例，并提出的模型达到了98.6％的精度，精度得分为97.8％，跨透明率较低。与其他方法（例如相邻特征传播网络（AFPNET），基于面膜区域的CNN（Mask RCNN），Yolov5）和Fourier CNN（FCNN）相比，提出的模型在检测脑肿瘤方面表现更好。
The growth of abnormal cells in the brain's tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient's survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient's life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors.
