The classification and segmentation of the tumor is an interesting area that differentiates the tumorous cells and the non-tumorous cells to identify the tumor level. The segmentation from MRI is a challenge because of its varying sizes of images and huge datasets. Different techniques were developed in the literature for brain tumor classification but due to accuracy and ineffective decision making, the existing techniques failed to provide improved classification. This work introduces an optimized deep learning mechanism; named Dolphin-SCA based Deep CNN, to improve the accuracy and to make effective decisions in classification. Initially, the input MRI images are given to the pre-processing and then, subjected to the segmentation process. The segmentation process is carried out using a fuzzy deformable fusion model with Dolphin Echolocation based Sine Cosine Algorithm (Dolphin-SCA). Then, the feature extraction process is performed based on power LDP and statistical features, like mean, variance, and skewness. The extracted features are used in the Deep Convolution Neural Network (Deep CNN) for performing the brain tumor classification with Dolphin-SCA as the training algorithm. The experimentation is performed using the MRI images taken from the BRATS database and SimBRATS, and the proposed technique has shown superior performance with a maximum accuracy of 0.963.

肿瘤的分类和分割是一个有趣的领域，可以区分肿瘤细胞和非肿瘤细胞以鉴定肿瘤水平。MRI的分割是一个挑战，因为它的图像尺寸和巨大的数据集各不相同。文献中针对脑肿瘤分类开发了不同的技术，但是由于准确性和决策无效，现有技术未能提供改进的分类。这项工作引入了优化的深度学习机制；命名为基于Dolphin-SCA的Deep CNN，以提高准确性并做出有效的分类决策。首先，将输入的MRI图像进行预处理，然后进行分割处理。使用基于海豚回声定位的正弦余弦算法（Dolphin-SCA）的模糊可变形融合模型执行分割过程。然后，基于功率LDP和统计特征（例如均值，方差和偏度）执行特征提取过程。提取的特征用于深度卷积神经网络（Deep CNN）中，以Dolphin-SCA作为训练算法执行脑肿瘤分类。使用从BRATS数据库和SimBRATS中获取的MRI图像进行了实验，所提出的技术显示了卓越的性能，最大精度为0.963。提取的特征用于深度卷积神经网络（Deep CNN）中，以Dolphin-SCA作为训练算法执行脑肿瘤分类。使用从BRATS数据库和SimBRATS中获取的MRI图像进行了实验，所提出的技术显示了卓越的性能，最大精度为0.963。提取的特征用于深度卷积神经网络（Deep CNN）中，以Dolphin-SCA作为训练算法执行脑肿瘤分类。使用从BRATS数据库和SimBRATS中获取的MRI图像进行了实验，所提出的技术显示了卓越的性能，最大精度为0.963。