Bhamidipati Kishore, Anuradha G, Muppidi Satish, Anjali Devi S
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Department of Computer Science and Engineering, Siddhartha Academy of Higher Education, Vijayawada, India.
Network. 2025 Jun 23:1-35. doi: 10.1080/0954898X.2025.2513690.
The anomalous enlargement of brain cells is known as Brain Tumour (BT), which can cause serious damage to different blood vessel and nerve in human body. A precise and early detection of BT is foremost important to eliminate severe illness. Thus, a SpinalNet Visual Geometry Group-16 (Spinal VGG-16-Net) is introduced for early BT detection. At first, Magnetic Resonance Imaging (MRI) of image obtained from data sample is subjected to image denoising by bilateral filter. Then, BT area is segmented from the image using entropy-based Kapur thresholding technique, where threshold values are ideally selected by Gradient Energy Valley Optimization (GEVO), which is designed by incorporating Energy Valley Optimization (EVO) with Stochastic Gradient Descent (SGD) algorithm. Then, process of image augmentation is worked and later, feature extraction is performed to mine the most significant features. Finally, BT is detected using proposed Spinal VGG-16Net, which is devised by combining both SpinalNet and VGG-16 Net. The Spinal VGG-16-Net is compared with some of the existing schemes, and it attained maximum accuracy of 92.14%, True Positive Rate (TPR) of 93.16%, True Negative Rate (TNR) of 91.35%, Negative Predictive Value (NPV) 89.73%, and Positive Predictive Value (PPV) o of 92.13%.
脑细胞的异常增大被称为脑肿瘤（BT），它会对人体的不同血管和神经造成严重损害。对脑肿瘤进行精确的早期检测对于消除严重疾病至关重要。因此，引入了一种用于早期脑肿瘤检测的脊髓网络视觉几何组16（脊髓VGG - 16网络）。首先，对从数据样本中获取的图像进行磁共振成像（MRI），并通过双边滤波器进行图像去噪。然后，使用基于熵的卡普尔阈值技术从图像中分割出脑肿瘤区域，其中阈值理想地通过梯度能量谷优化（GEVO）选择，该优化是通过将能量谷优化（EVO）与随机梯度下降（SGD）算法相结合来设计的。接着，进行图像增强处理，随后进行特征提取以挖掘最重要的特征。最后，使用所提出的脊髓VGG - 16网络检测脑肿瘤，该网络是通过结合脊髓网络和VGG - 16网络设计的。将脊髓VGG - 16网络与一些现有方案进行比较，其获得的最大准确率为92.14%，真阳性率（TPR）为93.16%，真阴性率（TNR）为91.35%，阴性预测值（NPV）为89.73%，阳性预测值（PPV）为92.13%。
