Centre for Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
Department of Computer Science and Engineering, Dhaka University of Engineering and Technology, Gazipur, Gazipur 1707, Bangladesh.
Biosensors (Basel). 2023 Feb 7;13(2):238. doi: 10.3390/bios13020238.
Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system.
利用自组织运算神经网络（Self-ONN）构建用于微波脑图像分类的八层轻量级分类器模型
利用重建的微波脑（RMB）图像对脑肿瘤进行计算机分类，对于脑疾病的检查和观察非常重要。在本文中，提出了一种名为微波脑图像网络（MBINet）的八层轻量级分类器模型，该模型使用自组织运算神经网络（Self-ONN）将重建的微波脑（RMB）图像分为六类。首先，实现了一个基于实验天线传感器的微波脑成像（SMBI）系统，并采集 RMB 图像以创建图像数据集。该数据集共包含 1320 张图像：非肿瘤 300 张，单个恶性和良性肿瘤各 215 张，双良性肿瘤和双恶性肿瘤各 200 张，单个良性和单个恶性肿瘤各 190 张。然后，使用图像缩放和归一化技术对图像进行预处理。之后，对数据集应用扩充技术，以使每个折叠的训练图像数量达到 13200 张，用于 5 折交叉验证。MBINet 模型经过训练，在使用原始 RMB 图像进行六类分类时，其准确率、精度、召回率、F1 得分和特异性分别达到 96.97%、96.93%、96.85%、96.83%和 97.95%。MBINet 模型与四个 Self-ONNs、两个香草卷积神经网络（vanilla CNNs）、ResNet50、ResNet101 和 DenseNet201 预训练模型进行了比较，显示出更好的分类结果（几乎 98%）。因此，MBINet 模型可用于 SMBI 系统中可靠地对 RMB 图像中的肿瘤进行分类。
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