CSE, Indira Gandhi Delhi Technical University for Women, New Delhi, India.
Department of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Sci Rep. 2023 Sep 11;13(1):14938. doi: 10.1038/s41598-023-41407-8.
The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. The highest accuracy achieved was 95.01% by Net-2. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.
脑肿瘤是一种对大脑极具危害性的疾病，其特征是异常细胞团的不受控制生长。早期脑肿瘤检测对于准确诊断和有效治疗计划至关重要。在本文中，我们提出了一种基于卷积神经网络（CNN）的图神经网络（GNN）模型，该模型使用 Kaggle 上公开的脑肿瘤数据集来预测一个人是否患有脑肿瘤，如果是，那么是哪种类型（脑膜瘤、垂体瘤或神经胶质瘤）。本研究和所提出的模型的目的是提供一种解决方案，以解决图像数据中非欧式距离的不考虑问题以及传统模型无法基于像素邻近距离学习像素相似性的问题。为了解决这个问题，我们提出了一种基于图的卷积神经网络（GCNN）模型，并且发现该模型解决了图像中考虑非欧式距离的问题。我们旨在使用一种新的技术来提高脑肿瘤检测和分类的准确性，该技术结合了 GNN 和一个 26 层的 CNN，该 CNN 接收一个使用图卷积操作预先卷积的图输入。图卷积的目的是通过结合来自附近节点的信息来修改节点特征（与每个节点相关联的数据）。使用标准的预计算邻接矩阵，输入图会更新为局部邻居节点的平均值，这些节点携带有关肿瘤的区域信息。这些修改后的图作为输入矩阵提供给带有批量归一化和随机失活层的标准 26 层 CNN。提出了五个不同的网络，分别是 Net-0、Net-1、Net-2、Net-3 和 Net-4，发现 Net-2 优于其他网络，即 Net-0、Net-1、Net-3 和 Net-4。Net-2 实现的最高精度为 95.01%。鉴于其当前的有效性，我们提出的模型代表了一种用于对疑似患有脑肿瘤的患者进行脑肿瘤统计检测的重要替代方案。
