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As of Globocan-2020 statistics, more than 0.4 million new kidney cancer/tumor cases have been listed and 0.2 million deaths were aroused due to it across the world. Hence, Kidney tumor investigation and diagnosis is one of the prime cancer treatment processes in medical field. Manual identification of kidney tumor from clinical scan images such like CT and MRI may lead to affect the diagnosis process accuracy. Therefore, semi-automated and fully-automated methods have been developed tremendously since past decade by using image segmentation approaches, convolutional neural network (CNN) and deep learning models to locate the kidney cancer/tumor from medical images and these approaches helps the experts in clinical diagnosis process. Therefore, here a detailed comparative report in terms of dice similarity index score (DSC) has been presented on deep learning-based kidney cancer/tumor segmentation approaches made by various researchers.

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Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT Images

  • 会议论文

  • 发表日期：

    2025年

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  • Prudhvi Raj Budumuru,
  • P. Murugapandiyan,
  • Kalva Sri Rama Krishna

摘要

As of Globocan-2020 statistics, more than 0.4 million new kidney cancer/tumor cases have been listed and 0.2 million deaths were aroused due to it across the world. Hence, Kidney tumor investigation and diagnosis is one of the prime cancer treatment processes in medical field. Manual identification of kidney tumor from clinical scan images such like CT and MRI may lead to affect the diagnosis process accuracy. Therefore, semi-automated and fully-automated methods have been developed tremendously since past decade by using image segmentation approaches, convolutional neural network (CNN) and deep learning models to locate the kidney cancer/tumor from medical images and these approaches helps the experts in clinical diagnosis process. Therefore, here a detailed comparative report in terms of dice similarity index score (DSC) has been presented on deep learning-based kidney cancer/tumor segmentation approaches made by various researchers.