In today's world, liver cancers are one of the mainly popular cancers occurring in the human body. The greater part of liver carcinomas is more prone to alcohol‐related hepatitis and cirrhosis conditions. Moreover, there is another form of cancer namely, metastatic liver cancer, where the tumor is initiated from other organs and extends to the liver. Early and premature diagnosis of liver cancer is necessary as it tends to improvise life expectancy. Nowadays, discriminating the liver and tumor parts from medical images with the aid of completely automated computer‐aided software is a more challenging task, since the liver disease can vary from person to person. This article attempts to implement the novel liver tumor segmentation and classification model using the optimization driven segmentation and classification model. The developed model carries out the task in five steps (a) Pre‐processing, (b) liver segmentation, (c) tumor segmentation, (d) feature extraction, and (e) classification. At first, the gathered CT images are subjected to pre‐processing with three steps that follow contrast enhancement by histogram equalization and noise filtering by the median filter. Next to the pre‐processing of the image, the liver is segmented from the CT abdominal image using adaptive thresholding pursued by level set segmentation. Further, a modified algorithm termed as Fuzzy Centroid‐based Region Growing Algorithm with tolerance optimization is developed and used for the tumor segmentation. From the segmented tumor image, three sets of features like gray‐level co‐occurrence matrix (GLCM), shape features, and local binary pattern (LBP) is utilized for the classifier training. In the classification side, two deep learning algorithms are used: recurrent neural network (RNN), and convolutional neural network (CNN). The tumor segmented image is given as input to the CNN, and the extracted features are given as input to the RNN. As an improvement, an optimized hybrid classifier is adopted for the hidden neuron optimization. Moreover, an improved meta‐heuristic algorithm called opposition‐based spotted hyena optimization (O‐SHO) is introduced to perform the optimized segmentation and classification. The experimental results show that the overall accuracy attained by the proposed model is efficient, less sensitive to noise, and performs superior on a diverse set of CT images.

中文翻译：

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使用基于对立的斑点鬣狗优化来优化肝肿瘤的分割和分类

在当今世界，肝癌是人体中最流行的癌症之一。大部分肝癌更容易发生与酒精有关的肝炎和肝硬化。此外，还有另一种癌症，即转移性肝癌，其中肿瘤是从其他器官开始并延伸到肝脏。早期和过早诊断肝癌是必要的，因为它往往会缩短预期寿命。如今，借助全自动计算机辅助软件将肝脏和肿瘤部位与医学图像区分开是一项更具挑战性的任务，因为肝脏疾病可能因人而异。本文尝试使用优化驱动的分割和分类模型来实现新型肝肿瘤分割和分类模型。所开发的模型分五个步骤执行任务：（a）预处理，（b）肝分割，（c）肿瘤分割，（d）特征提取和（e）分类。首先，对采集的CT图像进行三个步骤的预处理，然后通过直方图均衡增强对比度，并通过中值滤波器进行噪声过滤。在图像的预处理之后，使用通过水平集分割进行的自适应阈值分割，从CT腹部图像中分割出肝脏。此外，还开发了一种具有公差优化功能的改进算法，称为基于模糊质心的区域增长算法，并将其用于肿瘤分割。从分割的肿瘤图像中，可以得出三组特征，例如灰度共生矩阵（GLCM），形状特征，局部二值模式（LBP）用于分类器训练。在分类方面，使用了两种深度学习算法：递归神经网络（RNN）和卷积神经网络（CNN）。肿瘤分割图像作为CNN的输入，提取的特征作为RNN的输入。作为改进，对隐藏神经元优化采用了优化的混合分类器。此外，引入了一种改进的元启发式算法，称为基于对立的斑点鬣狗优化（O-SHO），以执行优化的分割和分类。实验结果表明，所提出的模型所获得的整体精度是有效的，对噪声不那么敏感，并且在各种CT图像上均具有出色的表现。递归神经网络（RNN）和卷积神经网络（CNN）。肿瘤分割图像作为CNN的输入，提取的特征作为RNN的输入。作为改进，对隐藏神经元优化采用了优化的混合分类器。此外，引入了一种改进的元启发式算法，称为基于对立的斑点鬣狗优化（O-SHO），以执行优化的分割和分类。实验结果表明，所提出的模型所获得的整体精度是有效的，对噪声不那么敏感，并且在各种CT图像上均具有出色的表现。递归神经网络（RNN）和卷积神经网络（CNN）。肿瘤分割图像作为CNN的输入，提取的特征作为RNN的输入。作为改进，对隐藏神经元优化采用了优化的混合分类器。此外，引入了一种改进的元启发式算法，称为基于对立的斑点鬣狗优化（O-SHO），以执行优化的分割和分类。实验结果表明，所提出的模型所获得的整体精度是有效的，对噪声不那么敏感，并且在各种CT图像上均具有出色的表现。隐神经元优化采用了优化的混合分类器。此外，引入了一种改进的元启发式算法，称为基于对立的斑点鬣狗优化（O-SHO），以执行优化的分割和分类。实验结果表明，所提出的模型所获得的整体精度是有效的，对噪声不那么敏感，并且在各种CT图像上均具有出色的表现。隐神经元优化采用了优化的混合分类器。此外，引入了一种改进的元启发式算法，称为基于对立的斑点鬣狗优化（O-SHO），以执行优化的分割和分类。实验结果表明，所提出的模型所获得的整体精度是有效的，对噪声不那么敏感，并且在各种CT图像上均具有出色的表现。