A computer vision approach for the classification of multi liver tumor using computed tomography scan.

This study describes the potential of computer vision (CV) approaches for liver tumor classification. Two-dimensional (2D) computed Tomography (CT) images dataset of benign and malignant liver tumors that were cholangiocarcinoma, focal nodular hyperplasia, hepatic adenoma, hemangioma, hepatoblastoma, and hepatocellular carcinoma was acquired for this study. The CT dataset comprising 150 images, each sized at (512 × 512), encompassing various types of liver tumors. This dataset consisted of a total of 900 (150 × 6) CT images representing six benign and malignant liver tumor types. To enhance data quality, a Mean filter was applied for noise reduction, followed by the selection of two regions of interest (ROIs) from each liver image. Subsequently, the preprocessed data was subjected to feature extraction, resulting in 67 multi-features per image, incorporating histogram, spectral, and texture features. From these features, 21 optimized multi-features were derived through the implementation of a correlation-based feature selection (CFS) algorithm. These optimized multi-features formed the basis for analysis and were fed into six classifiers: multilayer perceptron (MLP), logistic regression, random subspace, decision tree, produce error reduction, and multiclass classifier. The performance evaluation of these classifiers was conducted using 10-fold cross-validation techniques. The MLP showed a better accuracy of 97.67% on the optimized feature dataset among all the deployed CV classifiers. The experimental findings indicated that the suggested approach was systematic and resilient, offering valuable assistance to radiologists in detecting liver tumor diseases through CT Dataset images, even amid differing imaging standards.