Chaitanya Durbhakula M K, Aouthu Srilakshmi, Dhanalakshmi Narra, Srinivas Yerram, Dhanikonda Srinivasa Rao, Chinna Rao B
Department of Electronics and Communication Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India.
Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.
Microsc Res Tech. 2025 Apr;88(4):1102-1114. doi: 10.1002/jemt.24767. Epub 2024 Dec 16.
Brain tumor is a most dangerous disease and requires accurate diagnosis in a short period to ensure the best treatment. Traditional methods for brain tumor classification (BTC) are quite effective, even though usually resulting in clinical manual analysis, which takes more time and prone to errors. Initially, the input image is collected from Brain Tumor dataset. The gathered image is given to preprocessing. In preprocessing stage, trust-based distributed set-membership filtering (TDSF) is used to remove the noise. The preprocessed output is fed to the quaternion offset linear canonical transform (QOLCT) for Grayscale statistic and Haralick texture features extraction. Then the extracted features are fed to the Semantic-Preserved Generative Adversarial Network (SPGAN) for classifying the brain tumor into Glioma, Meningioma and Pituitary. Finally, Hunger Games Search Optimization (HGSO) is used to enhance the weight parameters of SPGAN. The proposed BTC-SPGAN-HGSO method attains the accuracies of 99.72% for Glioma, 99.65% for Meningioma, 99.52% for Pituitary and lowest MSE values across all tumor types, with 0.45% for Glioma, 0.39% for Meningioma, and 0.5% for Pituitary, which performs better than existing models. The simulation results highlight the effectiveness of the proposed BTC-SPGAN-HGSO approach in improving the accuracy of BTC and assist neurologists and physicians make exact decisions of diagnostic.
脑肿瘤是一种极其危险的疾病，需要在短时间内进行准确诊断以确保最佳治疗效果。传统的脑肿瘤分类（BTC）方法相当有效，尽管通常需要临床人工分析，这会花费更多时间且容易出错。最初，从脑肿瘤数据集中收集输入图像。将收集到的图像进行预处理。在预处理阶段，使用基于信任的分布式集成员滤波（TDSF）去除噪声。将预处理后的输出输入到四元数偏移线性规范变换（QOLCT）中，以提取灰度统计特征和哈氏纹理特征。然后，将提取的特征输入到语义保留生成对抗网络（SPGAN）中，将脑肿瘤分类为胶质瘤、脑膜瘤和垂体瘤。最后，使用饥饿游戏搜索优化（HGSO）来增强SPGAN的权重参数。所提出的BTC-SPGAN-HGSO方法在胶质瘤分类上的准确率达到99.72%，脑膜瘤为99.65%，垂体瘤为99.52%，并且在所有肿瘤类型中均具有最低的均方误差值，胶质瘤为0.45%，脑膜瘤为0.39%，垂体瘤为0.5%，其性能优于现有模型。仿真结果突出了所提出的BTC-SPGAN-HGSO方法在提高脑肿瘤分类准确率方面的有效性，并有助于神经科医生和内科医生做出准确的诊断决策。
