Reddy Panyala Amarendra, Manickam Baskar
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, India.
Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, India.
Electromagn Biol Med. 2024 Oct;43(4):205-219. doi: 10.1080/15368378.2024.2375266. Epub 2024 Jul 30.
Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this manuscript.Here, the input imagesare amassed from brain tumor dataset. Then the input images are preprocesssed using Range - Doppler Matched Filter (RDMF) for improving the quality of the image. Then Ternary Pattern and Discrete Wavelet Transforms (TPDWT) is employed for feature extraction and focusing on white, gray mass, edge correlation, and depth features. The proposed method leverages Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDS-GNN) to categorize brain tumor images into Glioma, Meningioma, and Pituitary tumors. Finally, Coati Optimization Algorithm (COA) optimizes MCDS-GNN's weight parameters. The proposed MCDS-GNN-IBTC-CGA is empirically evaluated utilizing accuracy, specificity, sensitivity, Precision, F1-score,Mean Square Error (MSE). Here, MCDS-GNN-IBTC-CGA attains 12.75%, 11.39%, 13.35%, 11.42% and 12.98% greater accuracy comparing to the existingstate-of-the-arts techniques, likeMRI brain tumor categorization utilizing parallel deep convolutional neural networks (PDCNN-BTC), attention-guided convolutional neural network for the categorization of braintumor (AGCNN-BTC), intelligent driven deep residual learning method for the categorization of braintumor (DCRN-BTC),fully convolutional neural networks method for the classification of braintumor (FCNN-BTC), Convolutional Neural Network and Multi-Layer Perceptron based brain tumor classification (CNN-MLP-BTC) respectively.
脑肿瘤类别的高效准确分类在医学成像中仍然是一项严峻挑战。尽管现有技术已取得进展，但其对通用特征的依赖往往导致次优结果。为克服这些问题，本文提出了基于高效不变特征中心增长分析的多模态对比域共享生成对抗网络用于改进脑肿瘤分类（MCDS-GNN-IBTC-CGA）。在此，输入图像取自脑肿瘤数据集。然后使用距离 - 多普勒匹配滤波器（RDMF）对输入图像进行预处理以提高图像质量。接着采用三元模式和离散小波变换（TPDWT）进行特征提取，并关注白色、灰色肿块、边缘相关性和深度特征。所提出的方法利用多模态对比域共享生成对抗网络（MCDS-GNN）将脑肿瘤图像分类为胶质瘤、脑膜瘤和垂体瘤。最后，浣熊优化算法（COA）优化MCDS-GNN的权重参数。所提出的MCDS-GNN-IBTC-CGA通过准确率、特异性、灵敏度、精确率、F1分数、均方误差（MSE）进行实证评估。在此，与现有最先进技术相比，如利用并行深度卷积神经网络进行脑肿瘤分类（PDCNN-BTC）、用于脑肿瘤分类的注意力引导卷积神经网络（AGCNN-BTC）、用于脑肿瘤分类的智能驱动深度残差学习方法（DCRN-BTC）、用于脑肿瘤分类的全卷积神经网络方法（FCNN-BTC）、基于卷积神经网络和多层感知器的脑肿瘤分类（CNN-MLP-BTC），MCDS-GNN-IBTC-CGA的准确率分别提高了12.75%、1
