Our proposed research technique intends to provide an effective liver magnetic resonance imaging (MRI) and computed tomography (CT) scan image classification which would play a significant role in medical dataset especially in feature selection and classification. There are a number of existing research works classifying the liver tumor disease. Early detection of liver tumor will help the patients to get cured rapidly. Our proposed research focuses on the classification of medical images with respect to the classification technique artificial neural network (ANN) to classify an image as normal or abnormal. In the pre-processing step, the input image is selected from the database and adaptive median filtering is used for noise removal. For better enhancement, histogram equalization (HE) is done in the noise-removed images. In the pre-processed images, the texture feature such as gray-level co-occurrence matrix (GLCM) and statistical features are extracted. From the extensive feature set, optimal features are selected using the optimal kernel K-means (OKK-means) clustering algorithm along with the oppositional firefly algorithm (OFA). The proposed method obtained 97.5% accuracy in the classification when compared to the existing method.

中文翻译：

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使用多核K均值聚类在肝脏MRI和CT图像上对肿瘤进行有效的分割和分类。

我们提出的研究技术旨在提供有效的肝磁共振成像（MRI）和计算机断层扫描（CT）扫描图像分类，这将在医学数据集中，尤其是在特征选择和分类中发挥重要作用。现有许多对肝肿瘤疾病进行分类的研究工作。早期发现肝肿瘤将有助于患者快速治愈。我们提出的研究重点是针对医学图像的分类，采用分类技术人工神经网络（ANN）将图像分类为正常或异常。在预处理步骤中，从数据库中选择输入图像，并将自适应中值滤波用于噪声去除。为了获得更好的增强效果，在去除噪声的图像中进行了直方图均衡（HE）。在预处理的图像中，提取诸如灰度共现矩阵（GLCM）之类的纹理特征和统计特征。从最佳功能集中，使用最佳内核K均值（OKK-means）聚类算法以及对立萤火虫算法（OFA）选择最佳特征。与现有方法相比，该方法在分类中的准确率为97.5％。