Semantic Segmentation of Kidney Tumors Using Variants of U-Net Architecture
Keywords:Kidney-Tumor Segmentation, U-Net architecture, Attention U-Net Architecture, Ensemble, Computed Tomography, Intersection Over Union
Kidney Cancer is one of the most prevalent diseases that is more common in men than in women. Detecting kidney tumors at an early stage has been found to increase survival rates of patients. It is therefore important to accurately segment tumors in Computed Tomography(CT) images. To assist in early detection of kidney tumors in CT images, we present a method for segmenting kidney tumors using deep convolutional neural networks. Predicted models using U-Net and Attention U-Net architectures are ensemble for effective tumor segmentation. Experimental and visual results obtained using the KiTS2019 dataset clearly demonstrate the enhanced Intersection Over Union(IoU) score of the ensemble model.
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Copyright (c) 2022 Geethanjali T M, Minavathi, Dinesh M S
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