In today's world of health care, brain tumor detection has become common. However, the manual brain tumor classification approach is time-consuming. So Deep Convolutional Neural Network (DCNN) is used by many researchers in the medical field for making accurate diagnoses and aiding in the patient's treatment. The traditional techniques have problems such as overfitting and the inability to extract necessary features. To overcome these problems, we developed the Topological Data Analysis based Improved Persistent Homology (TDA-IPH) and Convolutional Transfer learning and Visual Recurrent learning with Elephant Herding Optimization hyper-parameter tuning (CTVR-EHO) models for brain tumor segmentation and classification. Initially, the Topological Data Analysis based Improved Persistent Homology is designed to segment the brain tumor image. Then, from the segmented image, features are extracted using TL via the AlexNet model and Bidirectional Visual Long Short-Term Memory (Bi-VLSTM). Next, elephant Herding Optimization (EHO) is used to tune the hyperparameters of both networks to get an optimal result. Finally, extracted features are concatenated and classified using the softmax activation layer. The simulation result of this proposed CTVR-EHO and TDA-IPH method is analyzed based on precision, accuracy, recall, loss, and F score metrics. When compared to other existing brain tumor segmentation and classification models, the proposed CTVR-EHO and TDA-IPH approaches show high accuracy (99.8%), high recall (99.23%), high precision (99.67%), and high F score (99.59%).

翻译：在当今的医疗保健领域，脑肿瘤检测已变得十分普遍。然而，人工脑肿瘤分类方法耗时较长。因此，许多医学领域的研究者采用深度卷积神经网络（DCNN）来进行精确诊断并辅助患者治疗。传统技术存在过拟合及无法提取必要特征等问题。为克服这些困难，我们开发了基于拓扑数据分析的改进持续同调（TDA-IPH）模型，以及结合卷积迁移学习、视觉循环学习与象群优化超参数调谐（CTVR-EHO）的模型，用于脑肿瘤分割与分类。首先，基于拓扑数据分析的改进持续同调被设计用于分割脑肿瘤图像。随后，通过AlexNet模型和双向视觉长短期记忆网络（Bi-VLSTM）的迁移学习从分割图像中提取特征。接着，使用象群优化（EHO）对两个网络的超参数进行调优以获得最佳结果。最后，将提取的特征进行拼接，并利用softmax激活层进行分类。所提出的CTVR-EHO与TDA-IPH方法的仿真结果基于精确率、准确率、召回率、损失和F分数等指标进行分析。与现有的其他脑肿瘤分割和分类模型相比，所提出的CTVR-EHO与TDA-IPH方法展现出高准确率（99.8%）、高召回率（99.23%）、高精确率（99.67%）和高F分数（99.59%）。