The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W12
https://doi.org/10.5194/isprs-archives-XLII-2-W12-231-2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-231-2019
09 May 2019
 | 09 May 2019

DETECTING PROTRUSION LESION IN DIGESTIVE TRACT USING A SINGLE-STAGE DETECTION METHOD

L. Wang, S. Wang, S. Huang, and C. Liu

Keywords: Protrusion Lesion, Deep Learning, Single-stage Method, Multi-scale Feature Layers

Abstract. The classification networks have already existed for a long time and achieve great success. However, in biomedical image processing, classifying normal and abnormal ones only is not enough clinically, the desired output should include localization, i.e., where the lesion is. In this paper, we present a method for detecting protrusion lesion in digestive tract. We use a deep learning-based model to build a computer-aided diagnosis system to help doctors examine the intestinal diseases. Learn from existing detection method, one-stage and two-stage detection algorithm, a new network suitable for protrusion lesion detection is proposed. We inherit the method of anchor generation in SSD, a fast single-stage object detector outperform R-CNN series in terms of speed. Multi-scale feature layers are assigned to generate different sizes of default anchor boxes. Different from the previous work, our method doesnt require additional preprocessing because the network can learn features autonomously. For the 256*256 input, our method achieves 73% AP, perform a novel way to detect protrusion lesions.