Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis-Reference-Cited by-同舟云学术

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Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis

Published:2023 Issue: Volume:11 Page:139798-139810
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sitanaboina S L Parvathi¹^ORCID,Beeram Satyanarayana Reddy²^ORCID,Jonnadula Harikiran³^ORCID,Paleti Lakshmikanth⁴^ORCID

Affiliation:

1. Department of CSE—Data Science, Vignan’s Nirula Institute of Technology and Science for Women, Guntur, Andhra Pradesh, India

2. Department of Computer Science and Engineering, KKR & KSR Institute of Technology & Sciences, Guntur, Andhra Pradesh, India

3. School of Computer Science and Engineering, Vellore Institute of Technology, VIT-AP University, Vijayawada, Andhra Pradesh, India

4. Department of Computer Science and Business System, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10348554.pdf?arnumber=10348554

Reference38 articles.

1. The Metabolic Basis of Kidney Cancer

2. Renal cell carcinoma: Diagnosis and management;Gray;Amer. Family Physician,2019

3. Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

4. Characterization of solid renal neoplasms using MRI-based quantitative radiomics features

5. U-Net: Convolutional Networks for Biomedical Image Segmentation

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