Abstract
Brain tumors have very high morbidity and mortality, and it is very time-consuming for clinicians to diagnose this disease. Computer-aided medical image analysis can improve the performance of tumor diagnosis and alleviate the pressure of clinicians. Most of the existing intelligent diagnosis platforms rely on the public cloud, which has high requirements for communication and network and can not provide offline operation. We propose a brain tumor segmentation framework based on deep learning and edge-cloud collaboration, which can realize computer-assisted medical diagnosis in both offline and online modes. We deploy segmentation network with different complexity in the edge and cloud, and the Dice coefficients in offline mode and online mode are 0.8098 and 0.8910 respectively. Compared with the offline mode, the average values of Dice, PPV, and Sensitivity increased by 8.12\(\%\), 2.65\(\%\), and 7.31\(\%\) in the online mode. In addition, we also analyze the response time of the two modes. For the diagnosis of a patient, the offline mode and online mode take 27.610 s and 40.885 s respectively. Experimental data show that our method has an excellent performance in tumor diagnosis and response time, and the framework can be easily extended to other diseases diagnosis.
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Acknowledgements
The work was supported by Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University (Grant No. ZNJC201926), in part by the National Natural Science Foundation of China (62073248).
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Feng, S., Zhao, J., Zhao, W., Zhang, T. (2022). Brain Tumor Segmentation Framework Based on Edge Cloud Cooperation and Deep Learning. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_6
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