An Automatic Multi-Variate Multi-Class Feature Extraction, Ranking Based Joint Probabilistic Segmentation and Classification Framework for Multi-Class Liver Tumor Detection
Keywords:
Multi-variate Liver filtering, Multi-class Liver segmentation, deep learning classifiersAbstract
Automatic multivariate and multi-class tumor detection plays a crucial role in the diagnosis and treatment of large heterogeneous liver databases. However, existing liver segmentation models often encounter challenges such as multi-modal tumor detection, detecting tumors with varying shapes, liver over-segmentation, and difficulty in identifying tumors with different orientations and shapes. Moreover, the presence of noise in excessively segmented border regions can further complicate the segmentation and classification process, leading to inconsistent and inaccurate results. In this work, we propose novel approaches for multivariate liver filtering, multivariate feature extraction, and ranking. We employ efficient multivariate segmentation-based classification methods to enhance the overall detection of multi-modal tumors on large databases. The proposed Multi-variate Liver and Tumor Segmentation and Classification (MCMVLTSC) model efficiently classifies key tumor segmented regions with a high true positive rate and runtime efficiency (ms). To evaluate the performance of our proposed MCMVLTSC model compared to existing models, we utilize various statistical measures on diverse liver imaging databases. Experimental results demonstrate that the proposed model outperforms conventional models in terms of different statistical classification metrics and runtime efficiency.
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