论文标题
使用Yolov5深神经网络进行脑癌分割
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network
论文作者
论文摘要
异常脑细胞的扩展称为脑肿瘤。大脑的结构非常复杂，有几个地区控制着各种神经系统过程。大脑或头骨的任何部分都可以发展出脑肿瘤，包括大脑的保护性涂层，头骨的底部，脑干，鼻窦，鼻腔和许多其他地方。在过去的十年中，已经进行了计算机辅助脑肿瘤诊断领域的许多发展。最近，实例细分对众多计算机视觉应用引起了很多兴趣。它试图将各种ID分配给各种场景对象，即使它们是同一类的成员。通常，使用两个阶段的管道来执行实例分割。这项研究显示了使用Yolov5进行的脑癌分割。 Yolo将数据集作为图片格式和相应的文本文件。您只看一次（Yolo）是一种病毒且广泛使用的算法。 Yolo以其对象识别属性而闻名。您只看一次（YOLO）是一种流行的算法，已经流行了。 Yolo以其识别对象的能力而闻名。 Yolo V2，V3，V4和V5是专家近年来发表的一些Yolo最新版本。早期脑肿瘤检测是神经科医生和放射科医生所做的最重要的工作之一。但是，从磁共振成像（MRI）数据中手动识别和分割脑肿瘤可能很困难且容易出错。为了早期诊断病情，需要自动化的脑肿瘤检测系统。研究论文的模型有三个类。它们分别是脑膜瘤，垂体，神经胶质瘤。结果表明，我们的模型在M2 10 Core GPU的运行时使用时间达到了竞争精度。
An expansion of aberrant brain cells is referred to as a brain tumor. The brain's architecture is extremely intricate, with several regions controlling various nervous system processes. Any portion of the brain or skull can develop a brain tumor, including the brain's protective coating, the base of the skull, the brainstem, the sinuses, the nasal cavity, and many other places. Over the past ten years, numerous developments in the field of computer-aided brain tumor diagnosis have been made. Recently, instance segmentation has attracted a lot of interest in numerous computer vision applications. It seeks to assign various IDs to various scene objects, even if they are members of the same class. Typically, a two-stage pipeline is used to perform instance segmentation. This study shows brain cancer segmentation using YOLOv5. Yolo takes dataset as picture format and corresponding text file. You Only Look Once (YOLO) is a viral and widely used algorithm. YOLO is famous for its object recognition properties. You Only Look Once (YOLO) is a popular algorithm that has gone viral. YOLO is well known for its ability to identify objects. YOLO V2, V3, V4, and V5 are some of the YOLO latest versions that experts have published in recent years. Early brain tumor detection is one of the most important jobs that neurologists and radiologists have. However, it can be difficult and error-prone to manually identify and segment brain tumors from Magnetic Resonance Imaging (MRI) data. For making an early diagnosis of the condition, an automated brain tumor detection system is necessary. The model of the research paper has three classes. They are respectively Meningioma, Pituitary, Glioma. The results show that, our model achieves competitive accuracy, in terms of runtime usage of M2 10 core GPU.
