Purpose

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

Design/methodology/approach

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

Findings

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

Originality/value

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

中文翻译：

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优化神经网络和卷积神经网络的集成，用于自动脑肿瘤检测

目的

本文的目的是利用磁共振成像（MRI）图像的混合分类的有益概念，开发一种有效的脑肿瘤检测模型。脑肿瘤是最常见和最具破坏性的疾病，导致其最高等级的预期寿命非常短。大脑成像技术领域的知识和突飞猛进已经为评估和集中化对大脑解剖结构和手术的新感知发挥了至关重要的作用。图像处理系统在医学领域中已广泛使用，以增强早期诊断和治疗阶段。

设计/方法/方法

提出的检测模型涉及五个主要阶段，即图像预处理，肿瘤分割，特征提取，第三级离散小波变换（DWT）提取和检测。最初，使用不同的步骤对输入的MRI图像进行预处理，这些步骤称为图像缩放，基于熵的三边滤波和颅骨剥离。图像缩放用于调整图像大小，基于熵的三边滤波扩展以消除数字图像中的噪声。此外，通过Otsu阈值化来完成头骨剥离。在预处理的旁边，通过基于模糊质心的区域增长算法执行肿瘤分割。一旦从输入的MRI图像中分割出肿瘤，就完成了特征提取，重点是一阶和高阶统计量。在检测方面，采用神经网络和卷积神经网络相结合的混合分类器。在这里，NN将一阶和高阶统计量作为输入，而CNN将三阶DWT图像作为输入。作为改进，NN和CNN的隐藏神经元数量通过一种称为跨操作公鸡的鸡群优化（COR-CSO）的新型元启发式算法进行了优化。从优化的NN和CNN获得的结果的AND运算将输入图像分为正常和异常两类。最后，有价值的性能评估将证明所提出模型的性能在整个现有模型上都相当不错。NN将一阶和高阶统计量作为输入，而CNN则将三级DWT图像作为输入。作为改进，NN和CNN的隐藏神经元数量通过一种称为跨操作公鸡的鸡群优化（COR-CSO）的新型元启发式算法进行了优化。从优化的NN和CNN获得的结果的AND运算将输入图像分为正常和异常两类。最后，有价值的性能评估将证明所提出模型的性能在整个现有模型上都相当不错。NN将一阶和高阶统计量作为输入，而CNN则将三级DWT图像作为输入。作为改进，NN和CNN的隐藏神经元数量通过一种称为跨操作公鸡的鸡群优化（COR-CSO）的新型元启发式算法进行了优化。从优化的NN和CNN获得的结果的AND运算将输入图像分为正常和异常两类。最后，有价值的性能评估将证明所提出模型的性能在整个现有模型上都相当不错。NN和CNN的隐藏神经元数量通过一种称为跨操作公鸡的鸡群优化（COR-CSO）的新型元启发式算法进行了优化。从优化的NN和CNN获得的结果的AND运算将输入图像分为正常和异常两类。最后，有价值的性能评估将证明所提出模型的性能在整个现有模型上都相当不错。NN和CNN的隐藏神经元数量通过一种称为跨操作公鸡的鸡群优化（COR-CSO）的新型元启发式算法进行了优化。从优化的NN和CNN获得的结果的AND运算将输入图像分为正常和异常两类。最后，有价值的性能评估将证明所提出模型的性能在整个现有模型上都相当不错。

发现

从实验结果来看，建议的COR-CSO-NN + CNN的准确性似乎比支持向量机高18％，比NN高11.3％，比深度信念网络高22.9％，比CNN高15.6％，并且13.4比NN + CNN高出％，比粒子群优化-NN + CNN高出11.3％，比灰太狼优化-NN + CNN高出9.2％，比鲸鱼优化算法-NN + CNN高出5.3％，比CSO-NN高出3.5％ + CNN。最后，得出的结论是，建议的模型在使用MRI图像有效地检测脑肿瘤方面具有优势。

创意/价值

本文采用称为COR-CSO的最新优化算法，使用NN和CNN检测脑部肿瘤。这是第一项使用基于COR-CSO的优化进行准确的脑肿瘤检测的研究。