Mostafa Almetwally M, Zakariah Mohammed, Aldakheel Eman Abdullah
Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
Department of Computer Science, College of Computer and Information Science, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
Diagnostics (Basel). 2023 Apr 27;13(9):1562. doi: 10.3390/diagnostics13091562.
Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model's output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%.
脑肿瘤（BT）诊断是一个漫长的过程，需要放射科医生具备高超的技能和专业知识。随着患者数量的增加，需要处理的数据量也随之增长，使得以前的技术既昂贵又低效。许多学者研究了一系列用于识别和分类脑肿瘤的可靠且快速的技术。最近，深度学习（DL）方法因能够创建可快速可靠地诊断或分割脑肿瘤的计算机算法而受到欢迎。为了在医学图像中识别脑肿瘤，DL允许使用预训练的卷积神经网络（CNN）模型。脑肿瘤分割数据集中包含了所建议的脑肿瘤磁共振成像（MRI）图像，该数据集是作为开发和评估脑肿瘤分割与诊断算法的基准而创建的。该集合中有335张带注释的MRI图像。为了开发和测试脑肿瘤分割与诊断算法，制作了脑肿瘤分割（BraTS）数据集。在使用BraTS数据集进行脑肿瘤分割的模型构建过程中也采用了深度CNN。为了训练模型，使用了分类交叉熵损失函数和诸如Adam之类的优化器。最后，该模型的输出成功地在数据集中识别并分割了脑肿瘤，验证准确率达到了98%。
