HMSAM-UNet: A Hierarchical Multi-Scale Attention Module-Based Convolutional Neural Network for Improved CT Image Segmentation-Reference-Cited by-同舟云学术

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HMSAM-UNet: A Hierarchical Multi-Scale Attention Module-Based Convolutional Neural Network for Improved CT Image Segmentation

Published:2024 Issue: Volume:12 Page:79415-79427
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Liu Na¹,Lu Zhonghua¹^ORCID,Lian Wenyong²,Tian Min¹,Ma Chiyue¹,Peng Lijuan³

Affiliation:

1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China

2. General Hospital of the Third Division of Xinjiang Production and Construction Corps, Tumxuk, Xinjiang, China

3. College of Sciences, Shihezi University, Shihezi, China

Funder

Division City Key Areas of Science and Technology Research Program Projects

Division City Science and Technology Innovation Talent Program

in part by the Research Start-Up Funds for High-Level Talents at Shihezi University

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10535273.pdf?arnumber=10535273

Reference37 articles.

1. A Precise Analysis of Deep Learning for Medical Image Processing

2. Review of automated segmentation approaches for knee images

3. Artificial intelligence techniques for cancer detection in medical image processing: A review

4. FHI-Unet: Faster Heterogeneous Images Semantic Segmentation Design and Edge AI Implementation for Visible and Thermal Images Processing

5. Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis

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