Abstract
Gastric cancer is one of the malignant cancers with a very high fatal rate, and early detection plays an essential role in the treatment and improves the five-year 5-year survival rate. In this study, we an improved gastric cancer detection method in endoscopy image based on SSD (Single Shot MultiBox Detector). Our methods mainly aim to deal with the insufficient fusion of different semantic feature maps and the existence of semantic gaps during fusion in the SSD detector. To achieve these goals, we leverage a recurrent feature pyramid network, a multi-layer feature fusion module, and an auxiliary lesion segmentation branch. The experimental results on the gastric cancer dataset collected from the First Affiliated Hospital of Xiamen University show that the improved SSD algorithm can improve the mAP metric by 5.9% compared with the original SSD algorithm to reach 56%.
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Acknowledgement
This work is supported by the National Nature Science Foundation of China (No. 61876159, 61806172, 62076116, U1705286).
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Liu, M., Luo, Z., Cao, D., Lin, D., Su, S., Li, S. (2022). An Improved SSD-Based Gastric Cancer Detection Method. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_35
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DOI: https://doi.org/10.1007/978-981-19-4546-5_35
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