Document Type
Article
Disciplines
2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING
Abstract
Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.
DOI
https://doi.org/10.1109/JBHI.2023.3297227
Recommended Citation
Wu, Dongyuan; Tao, Junyi; Qin, Zhen; Mumtaz, Rao Asad; Qin, Jin; Yu, Linfang; and Courtney, Jane, "LightNet: A Novel Lightweight Convolutional Network for Brain Tumor Segmentation in Healthcare" (2023). Articles. 371.
https://arrow.tudublin.ie/engscheleart2/371
Funder
This work was supported in part by the National Natural Science Foundation of China (No.62072074, No.62076054, No.62027827, No.61902054, No.62002047), the Sichuan Science and Technology Innovation Platform and Talent Plan (No.2020JDJQ0020, No.2022JDJQ0039), the Sichuan Science and Technology Support Plan (No.2019YJ0636, No.2020YFSY0010, No.2021YFG0131, No.2022YFQ0045, No.2022YFS0220), and the Fundamental Research Funds for the Central Universities (No.ZYGX2021YGLH212
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Publication Details
https://ieeexplore.ieee.org/abstract/document/10195977/authors#authors
D. Wu et al., "LightNet: a novel lightweight convolutional network for brain tumor segmentation in healthcare," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2023.3297227.