使用混合卷积神经网络方法和RIDGELET变换检测和分类脑膜瘤肿瘤。
Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform.
发表日期：2023 Sep 04
作者：
B V Prakash, A Rajiv Kannan, N Santhiyakumari, S Kumarganesh, D Siva Sundhara Raja, J Jasmine Hephzipah, K MartinSagayam, Marc Pomplun, Hien Dang
来源：
Brain Structure & Function
摘要：
由于脑膜瘤的像素密度较低，与其他肿瘤比较起来，脑膜瘤的检测是最重要的任务。现代医学平台需要一个完全自动化的系统来进行脑膜瘤检测。因此，本研究提出了一种新颖且高效的混合卷积神经网络 (HCNN) 分类器，以区分脑膜瘤图像和非脑膜瘤图像。HCNN 分类技术包括 Ridgelet 变换、特征计算、分类器模块和分割算法。Ridgelet 变换改善了分解过程中的像素稳定性，并且通过 Ridgelet 的系数计算出特征。这些特征使用 HCNN 分类方法进行分类，并使用分割算法检测肿瘤像素。将提出的方法应用于 BRATS 2019 和 Nanfang 数据集上的脑膜瘤图像进行实验结果分析。提出的基于 HCNN 的脑膜瘤检测系统在 BRATS 2019 数据集上实现了 99.31% 的敏感性、99.37% 的特异性和 99.24% 的分割准确度。在 Nanfang 数据集的脑部磁共振成像 (MRI) 上，提出的 HCNN 技术实现了 99.35% 的敏感性、99.22% 的特异性和 99.04% 的分割准确度。提出的系统在 BRATS 2022 数据集上获得了 99.81% 的分类准确度、99.2% 的敏感性、99.7% 的特异性和 99.8% 的分割准确度。在本研究中，将提出的 HCNN 算法的实验结果与当今最先进的脑膜瘤检测算法进行了比较。© 2023. Springer Nature Limited.
The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study.© 2023. Springer Nature Limited.