Journal
JOURNAL OF IMAGING
Volume 8, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/jimaging8030055
Keywords
kidney tumor segmentation; deep learning; computerized tomography imaging; survey
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Deep learning models play a crucial role in kidney tumor segmentation, assisting clinicians in accurately identifying and segmenting tumors, and improving the efficacy of tumor treatment.
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. Simultaneously, researchers in the field of medical image segmentation employ DL approaches to solve problems such as tumor segmentation, cell segmentation, and organ segmentation. Segmentation of tumors semantically is critical in radiation and therapeutic practice. This article discusses current advances in kidney tumor segmentation systems based on DL. We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various strategies.
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