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使用带有堆叠集成学习的密集连接卷积网络增强脑肿瘤检测与分割

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

作者信息

Shaikh Asadullah, Amin Samina, Zeb Muhammad Ali, Sulaiman Adel, Al Reshan Mana Saleh, Alshahrani Hani

机构信息

Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.

Institute of Computing, Kohat University of Science and Technology, Kohat, 26000, Pakistan.

出版信息

Comput Biol Med. 2025 Mar;186:109703. doi: 10.1016/j.compbiomed.2025.109703. Epub 2025 Jan 24.

Abstract
  • Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications. BT segmentation with MRI remains challenging despite advancements in image acquisition techniques. Accurate detection and segmentation are essential for proper diagnosis and treatment planning. This study aims to enhance BT detection and segmentation accuracy and effectiveness of categorization through the implementation of an advanced stacking ensemble learning (SEL) approach. This study explores the efficiency of SEL architecture in augmenting the precision of BT segmentation. SEL, a prominent approach within the machine learning paradigm, combines the predictions of base-level models and improves the overall performance of predictions in order to reduce the errors and biases of each model. The proposed approach involves designing a stacked DenseNet201 as the meta-model called SEL-DenseNet201, complemented by six diverse base models such as mobile network version 3 (MobileNet-v3), 3-dimensional convolutional neural network (3D-CNN), visual geometry group network with 16 and 19 layers (VGG-16 and VGG-19), residual network with 50 layers (ResNet50), and Alex network (AlexNet). The strengths of the base models are calculated to capture distinct aspects of the BT MRI, aiming for enhanced segmentation performance. The proposed SEL-DenseNet201 is trained using BT MRI datasets. The augmentation techniques are applied to MRI scans to balance and enhance the model performance through the application of image enhancement and segmentation techniques. The proposed SEL-DenseNet201 achieves impressive results with an accuracy of 99.65 % and a dice coefficient of 97.43 %. These outcomes underscore the superiority of the proposed model over existing approaches. This study holds the potential to be an initial screening approach for early BT detection, with a high success rate.
摘要
  • 脑肿瘤(BT),无论良性还是恶性,都对人类健康构成重大影响,需要精确且早期的检测以实现成功治疗。分析磁共振成像(MRI)图像是BT诊断和分割的常用方法,但误诊会导致有效的医疗反应,影响患者生存率。最近的技术进步使基于深度学习的医学图像分析得到普及,利用迁移学习为各种应用重新使用预训练模型。尽管图像采集技术有所进步,但利用MRI进行BT分割仍然具有挑战性。准确的检测和分割对于正确的诊断和治疗规划至关重要。本研究旨在通过实施先进的堆叠集成学习(SEL)方法来提高BT检测、分割的准确性以及分类的有效性。本研究探讨了SEL架构在提高BT分割精度方面的效率。SEL是机器学习范式中的一种突出方法,它结合了基础模型的预测,并提高预测的整体性能,以减少每个模型的误差和偏差。所提出的方法包括设计一个堆叠的DenseNet201作为名为SEL-DenseNet201的元模型,并辅以六个不同的基础模型,如移动网络版本3(MobileNet-v3)、三维卷积神经网络(3D-CNN)、具有16层和19层的视觉几何组网络(VGG-16和VGG-19)、具有50层的残差网络(ResNet50)以及Alex网络(AlexNet)。计算基础模型的优势以捕捉BT MRI的不同方面,旨在提高分割性能。所提出的SEL-DenseNet201使用BT MRI数据集进行训练。通过应用图像增强和分割技术,对MRI扫描应用增强技术以平衡和提高模型性能。所提出的SEL-DenseNet201取得了令人印象深刻的结果,准确率为99.65%,骰子系数为97.43%。这些结果强调了所提出模型相对于现有方法的优越性。本研究有可能成为早期BT检测的初步筛查方法,成功率很高。

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