PDF(Pubmed)
Abstract：
Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.
摘要：
神经科学是一门快速发展的学科，旨在揭示人类大脑和思想的复杂运作。脑肿瘤,从非癌到恶性，由于存在100多种不同类型，因此构成了重大的诊断挑战。有效的治疗取决于早期对这些肿瘤的精确检测和分割。我们介绍了一种采用二进制卷积神经网络（BCNN）的尖端深度学习方法来解决这个问题。该方法用于分割10种最常见的脑肿瘤类型，并且是对仅限于分割四种类型的当前模型的显着改进。我们的方法从获取MRI图像开始，然后是详细的预处理阶段，其中图像使用自适应阈值方法和形态学运算进行二进制转换。这将为下一步准备数据，这是分割。分割识别肿瘤类型并根据其等级(等级I至等级IV)对其进行分类,并将其与健康脑组织区分开。我们还策划了一个独特的数据集，包括专门用于本研究的6,600张脑部MRI图像。我们提出的模型实现的整体性能为99.36%。我们模型的有效性被其卓越的性能指标所强调，达到99.40%的准确度，99.32%精度,99.45%召回,和一个99.28%的F-Measure在分割任务。
