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- W4214520917 endingPage "55" @default.
- W4214520917 startingPage "55" @default.
- W4214520917 abstract "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." @default.
- W4214520917 created "2022-03-02" @default.
- W4214520917 creator A5002862833 @default.
- W4214520917 creator A5054315649 @default.
- W4214520917 date "2022-02-25" @default.
- W4214520917 modified "2023-09-30" @default.
- W4214520917 title "Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art" @default.
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