Multi-Class Liver Tumor Detection Through Ranking-Based Probabilistic Segmentation And Automated Feature Extraction Framework

Effective automatic detection of multivariate and multi-class tumors is fundamental for accurately analyzing and handling diverse liver datasets. Current liver segmentation methods face significant challenges such as handling tumors of different modalities, shapes, and orientations, as well as issues with over-segmentation and incorrect tumor identification. Additionally, excessive randomness in segmented crossing areas can promote complication for the processing of segmentation as well as classification indicating to unreliable findings.To address these questions, we propose a new approach that incorporates advanced techniques for feature extraction, multivariate liver filtering, and ranking. Our solution utilizes capable classification methods under segmentation-based for increasing the detection for various tumor types in large datasets. Nevertheless,the model of Multi-Dimensional Liver and Tumor Segmentation and Classification developed is designed to accurately classify tumor-segmented sections, achieving extreme true positive (TP) rates in addition exceptional run-time productivity, measured in milliseconds.We validate the MCMVLTSC model through extensive testing using a range of statistical metrics across different liver imaging databases. The findings demonstrate that our model consistently delivers superior performance in classification accuracy and runtime efficiency compared to traditional methods.