This article exploits a new brain tumor classification model that includes five steps like (a) denoising, (b) skull stripping, (c) segmentation, (d) feature extraction and (e) classification. Initially, the image is subjected under the denoising process, where the noise removal procedure is carried out by employing the entropy‐based trilateral filter. Then, the denoised image is applied to the skull stripping process via Otsu thresholding and morphology segmentation. Subsequently, the next step is the segmentation, where the image is segmented by deploying the adaptive CLFAHE (contrast limited fuzzy adaptive histogram equalization) technique. Once the segmentation is completed, gray‐level co‐occurrence matrix (GLCM) based features are extracted. Finally, the extracted features are processed under hybrid classification model to attain enhanced classification rate. Here, hybrid classification hybrids two classifiers namely deep belief network (DBN) and Bayesian regularization classifier. The vital contribution of this research work exists in the optimal selection of hidden neurons in the DBN. Along with this, the membership function (bounding limits) of fuzzy logic is optimally selected. For this, a new lion exploration based whale optimization (LE‐WO) algorithm is proposed in this article that hybrids the concept of (lion algorithm) LA and (whale optimization algorithm) WOA. Finally, the performance of proposed LE‐WO is compared over the other methods in terms of accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1_score and Matthews correlation coefficient (MCC), False positive rate (FPR), False negative rate (FNR) and false discovery rate (FDR) and proves the betterments of proposed work. From the outcomes, the accuracy measure of proposed model at 60th population size is 1.98%, 1.81%, 1.32%, 3.46% and 0.75% better than PSO, FF, GWO, WOA and LA, respectively. Similarly, in 80th population size, the performance of the implemented model is 4.47%, 5.04%, 3.96%, 6.29% and 1.37% superior to PSO, FF, GWO, WOA and LA, respectively. Thus, the betterment of the adopted scheme is validated in an effective manner.

中文翻译：

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通过具有混合分类的自适应 CLFAHE 对脑肿瘤进行分割和分类

本文利用了一种新的脑肿瘤分类模型，该模型包括五个步骤，如 (a) 去噪、(b) 颅骨剥离、(c) 分割、(d) 特征提取和 (e) 分类。最初，对图像进行去噪过程，其中通过采用基于熵的三边滤波器进行噪声去除过程。然后，通过 Otsu 阈值和形态分割将去噪图像应用于头骨剥离过程。随后，下一步是分割，其中通过部署自适应 CLFAHE（对比度限制模糊自适应直方图均衡）技术对图像进行分割。分割完成后，提取基于灰度共生矩阵（GLCM）的特征。最后，提取的特征在混合分类模型下进行处理，以提高分类率。在这里，混合分类混合了两个分类器，即深度置信网络 (DBN) 和贝叶斯正则化分类器。这项研究工作的重要贡献在于 DBN 中隐藏神经元的最佳选择。与此同时，模糊逻辑的隶属函数（边界限制）被优化选择。为此，本文提出了一种新的基于狮子探索的鲸鱼优化（LE-WO）算法，它混合了（狮子算法）LA 和（鲸鱼优化算法）WOA 的概念。最后，在准确性、灵敏度、特异性、精确度、阴性预测值 (NPV)、F1_score 和马修斯相关系数 (MCC) 方面，将所提出的 LE-WO 的性能与其他方法进行了比较，假阳性率 (FPR)、假阴性率 (FNR) 和假发现率 (FDR) 并证明了拟议工作的改进。从结果来看，所提出模型在第 60 人口规模的准确度测量分别比 PSO、FF、GWO、WOA 和 LA 好 1.98%、1.81%、1.32%、3.46% 和 0.75%。同样，在第 80 人口规模中，实施模型的性能分别优于 PSO、FF、GWO、WOA 和 LA 4.47%、5.04%、3.96%、6.29% 和 1.37%。因此，采用的方案的改进以有效的方式得到验证。同样，在第 80 人口规模中，实施模型的性能分别优于 PSO、FF、GWO、WOA 和 LA 4.47%、5.04%、3.96%、6.29% 和 1.37%。因此，采用的方案的改进以有效的方式得到验证。同样，在第 80 人口规模中，实施模型的性能分别优于 PSO、FF、GWO、WOA 和 LA 4.47%、5.04%、3.96%、6.29% 和 1.37%。因此，采用的方案的改进以有效的方式得到验证。