Associate Professor, Department of Artificial Intelligence and Data Science, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India.
Assistant Professor, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India.
NMR Biomed. 2024 Dec;37(12):e5246. doi: 10.1002/nbm.5246. Epub 2024 Aug 28.
In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN-WHOA-BTD) is proposed. Here, the input imageries are gathered using BraTS, RemBRANDT, and Figshare datasets. Initially, the images are preprocessed to increase the quality of images and eliminate the unwanted noises. The preprocessing is performed with dual-tree complex wavelet transform (DTCWT). The image features like geodesic data and texture features like contrasts, energy, correlations, homogeneity, and entropy are extracted using multilayer dense net methods. Then, the extracted images are given to attentive dual residual generative adversarial network (ADRGAN) classifier for classifying the brain imageries. The ADRGAN weight parameters are tuned based on wild horse optimization algorithm (WHOA). The proposed method is executed in MATLAB. For the BraTS dataset, the ADRGAN-WHOA-BTD method achieved accuracy, sensitivity, specificity, F-measure, precision, and error rates of 99.85%, 99.82%, 98.92%, 99.76%, 99.45%, and 0.15%, respectively. Then, the proposed technique demonstrated a runtime of 13 s, significantly outperforming existing methods.
本文提出了一种基于野马优化算法优化的注意双残差生成对抗网络（ADRGAN-WHOA-BTD），用于脑肿瘤检测。在这里，使用 BraTS、RemBRANDT 和 Figshare 数据集来收集输入图像。最初，使用双树复小波变换（DTCWT）对图像进行预处理，以提高图像质量并消除不必要的噪声。使用多层密集网络方法提取图像特征，如测地距离数据和纹理特征，如对比度、能量、相关性、同质性和熵。然后，将提取的图像输入注意双残差生成对抗网络（ADRGAN）分类器进行分类。根据野马优化算法（WHOA）调整 ADRGAN 的权重参数。该方法在 MATLAB 中执行。对于 BraTS 数据集，ADRGAN-WHOA-BTD 方法的准确率、灵敏度、特异性、F 度量、精确率和误差率分别为 99.85%、99.82%、98.92%、99.76%、99.45%和 0.15%。然后，该技术的运行时间为 13s，明显优于现有方法。
