Liver cancer classification via deep hybrid model from CT image with improved texture feature set and fuzzy clustering based segmentation

One of the leading causes of death for people worldwide is liver cancer. Manually identifying the cancer tissue in the current situation is a challenging and time-consuming task. Assessing the tumor load, planning therapies, making predictions, and tracking the clinical response can all be done using the segmentation of liver lesions in Computed Tomography (CT) scans. In this paper we propose a new technique for liver cancer classification with CT image. This method consists of four stages like pre-processing, segmentation, feature extraction and classification. In the initial stage the input image will be pre processed for the quality enhancement. This preprocessed output will be subjected to the segmentation phase; here improved deep fuzzy clustering technique will be applied for image segmentation. Subsequently, the segmented image will be the input of the feature extraction phase, where the extracted features are named as Improved Gabor Transitional Pattern, Grey-Level Co-occurrence Matrix (GLCM), Statistical features and Convolutional Neural Network (CNN) based feature. Finally the extracted features are subjected to the classification stage, here the two types of classifiers used for classification that is Bi-GRU and Deep Maxout. In this phase we will apply the Crossover mutated COOT optimization (CMCO) for tuning the weights, So that we will improve the quality of the image. This proposed technique, present the best accuracy of disease identification. The CMCO gained the accuracy of 95.58%, which is preferable than AO = 92.16%, COA = 89.38%, TSA = 88.05%, AOA = 92.05% and COOT = 91.95%, respectively.