脑肿瘤的特点是异常细胞不受控制的生长，对人类健康构成重大威胁。早期检测对于成功治疗和改善患者预后至关重要。磁共振成像 (MRI) 是脑肿瘤的主要诊断工具，可提供大脑复杂结构的详细可视化。然而，肿瘤形状和位置的复杂性和可变性常常挑战医生在 MRI 图像上实现准确的肿瘤分割。精确的肿瘤分割对于有效的治疗计划和预后至关重要。为了应对这一挑战，我们提出了一种新颖的混合深度学习技术，即卷积神经网络和 ResNeXt101 (ConvNet-ResNeXt101)，用于自动肿瘤分割和分类。我们的方法从 BRATS 2020 数据集的数据采集开始，该数据集是具有相应肿瘤分割的 MRI 图像的基准集合。接下来，我们采用批量归一化来平滑和增强收集的数据，然后使用 AlexNet 模型进行特征提取。这涉及根据肿瘤形状、位置、形状和表面特征提取特征。为了选择信息最丰富的特征来进行有效的分割，我们利用了一种先进的元启发式算法，称为高级鲸鱼优化（AWO）。 AWO模仿座头鲸的捕猎行为来迭代搜索最优特征子集。利用选定的特征，我们使用 ConvNet-ResNeXt101 模型执行图像分割。这种深度学习架构结合了 ConvNet 和 ResNeXt101 的优势，ResNeXt101 是一种具有聚合残差连接的 ConvNet。最后，我们应用相同的 ConvNet-ResNeXt101 模型进行肿瘤分类，将分割后的肿瘤分为不同的类型。我们的实验证明，与现有方法相比，我们提出的 ConvNet-ResNeXt101 模型具有优越的性能，肿瘤核心类的准确率达到 99.27%，最短学习时间为 0.53 秒。版权所有：© 2024 Gunasekaran 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.Copyright: © 2024 Gunasekaran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.