标题： 基于TrAdaBoost和多分类器深度学习方法的脑肿瘤分类增强 显示英文标题

标题： Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches

摘要： 脑瘤因其快速生长和潜在的转移性而对健康构成严重威胁。 尽管医学影像技术取得了显著进步，但准确识别和表征这些肿瘤仍然是一项挑战。 本研究通过利用创新的TrAdaBoost方法论来增强脑瘤分割（BraTS2020）数据集，以提高脑瘤分类的效率和准确性。 我们的方法结合了最先进的深度学习算法，包括视觉Transformer（ViT）、胶囊神经网络（CapsNet）以及ResNet-152和VGG16等卷积神经网络（CNNs）。 通过在一个多分类器框架内整合这些模型，我们利用每种方法的优势实现更强大且可靠的肿瘤分类。 采用一种新颖的决策模板协同结合不同算法的输出，进一步提升分类准确性。 为了增强训练过程，我们将“脑瘤MRI数据集”作为源领域纳入，为模型训练提供额外数据并改善泛化能力。 我们的研究结果表明，在区分肿瘤与非肿瘤图像方面具有高准确性，这证明了我们的方法在医学影像领域的有效性。 本研究表明，先进的机器学习技术在早期和准确诊断脑瘤方面具有巨大潜力，最终可改善患者预后。 显示英文摘要

摘要： Brain tumors pose a serious health threat due to their rapid growth and potential for metastasis. While medical imaging has advanced significantly, accurately identifying and characterizing these tumors remains a challenge. This study addresses this challenge by leveraging the innovative TrAdaBoost methodology to enhance the Brain Tumor Segmentation (BraTS2020) dataset, aiming to improve the efficiency and accuracy of brain tumor classification. Our approach combines state-of-the-art deep learning algorithms, including the Vision Transformer (ViT), Capsule Neural Network (CapsNet), and convolutional neural networks (CNNs) such as ResNet-152 and VGG16. By integrating these models within a multi-classifier framework, we harness the strengths of each approach to achieve more robust and reliable tumor classification. A novel decision template is employed to synergistically combine outputs from different algorithms, further enhancing classification accuracy. To augment the training process, we incorporate a secondary dataset, "Brain Tumor MRI Dataset," as a source domain, providing additional data for model training and improving generalization capabilities. Our findings demonstrate a high accuracy rate in classifying tumor versus non-tumor images, signifying the effectiveness of our approach in the medical imaging domain. This study highlights the potential of advanced machine learning techniques to contribute significantly to the early and accurate diagnosis of brain tumors, ultimately improving patient outcomes.