Abstract
Computer-aided detection and segmentation of polyps present inside colon are quite challenging due to the large variations of polyps in features like shape, texture, size, and color, and the presence of various polyp-like structures during colonoscopy. In this paper, we apply a mask region-based convolutional neural network (Mask R-CNN) approach for the detection and segmentation of polyps in the images obtained from colonoscopy videos. We propose an efficient method to reduce the false positives in the computer-aided detection system. To achieve this, we rigorously train our model by selecting non-polyp regions in the image which have high probability of getting detected as a polyp. Using two colonoscopic frame datasets, we demonstrate the experimental results that show the significant reduction in the number of false positives by adding selected regions in our computer-aided polyp segmentation system.
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Jha, S., Jagtap, B., Mazumdar, S., Sinha, S. (2022). Computer-Aided Segmentation of Polyps Using Mask R-CNN and Approach to Reduce False Positives. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_10
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DOI: https://doi.org/10.1007/978-981-16-6624-7_10
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