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<p>Liver cancer remains a leading cause of cancer-related mortality worldwide, and timely, accurate classification of liver tumors in magnetic resonance imaging (MRI) is critical for improving patient outcomes. However, automated liver tumor classification is challenged by blurred tumor boundaries, variable lesion sizes, and heterogeneous image characteristics. To address these issues, we propose a novel Convolutional Gated Kronecker Network (CGKN) framework that integrates spatial and contextual feature learning for robust liver tumor classification in MRI. The methodology begins with preprocessing MRI images using an Adaptive Wiener Filter to enhance image quality and reduce noise, followed by precise segmentation of liver and lesion regions using a Dynamic Context Encoder Network. After extracting relevant features, the CGKN—combining Convolutional Neural Network-Gated Recurrent Unit and Deep Kronecker Network modules—classifies tumors into mild, moderate, or severe categories. This hybrid approach is designed to overcome the limitations of existing models, such as limited generalizability, high computational cost, and insufficient integration of spatial and contextual cues. Experimental evaluation on a benchmark MRI dataset demonstrates that the CGKN achieves an accuracy of 92.88%, a true positive rate of 91.64%, and a true negative rate of 91.56%, outperforming several state-of-the-art deep learning models. These findings highlight the potential of the CGKN framework as an effective and efficient solution for automated liver tumor classification in MRI, supporting more reliable computer-aided diagnosis and facilitating clinical decision-making.</p>

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A Deep Learning Framework for Enhanced Liver Tumor Classification Using Convolutional Gated Kronecker Network

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • B. Shilpa,
  • George Glan Devadhas,
  • T. Y. Satheesha

摘要

Liver cancer remains a leading cause of cancer-related mortality worldwide, and timely, accurate classification of liver tumors in magnetic resonance imaging (MRI) is critical for improving patient outcomes. However, automated liver tumor classification is challenged by blurred tumor boundaries, variable lesion sizes, and heterogeneous image characteristics. To address these issues, we propose a novel Convolutional Gated Kronecker Network (CGKN) framework that integrates spatial and contextual feature learning for robust liver tumor classification in MRI. The methodology begins with preprocessing MRI images using an Adaptive Wiener Filter to enhance image quality and reduce noise, followed by precise segmentation of liver and lesion regions using a Dynamic Context Encoder Network. After extracting relevant features, the CGKN—combining Convolutional Neural Network-Gated Recurrent Unit and Deep Kronecker Network modules—classifies tumors into mild, moderate, or severe categories. This hybrid approach is designed to overcome the limitations of existing models, such as limited generalizability, high computational cost, and insufficient integration of spatial and contextual cues. Experimental evaluation on a benchmark MRI dataset demonstrates that the CGKN achieves an accuracy of 92.88%, a true positive rate of 91.64%, and a true negative rate of 91.56%, outperforming several state-of-the-art deep learning models. These findings highlight the potential of the CGKN framework as an effective and efficient solution for automated liver tumor classification in MRI, supporting more reliable computer-aided diagnosis and facilitating clinical decision-making.