Optimized segmentation and classification for liver tumor segmentation and classification using opposition‐based spotted hyena optimization

Abstract

AbstractIn 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.