Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

Figure 1 Visible light image decomposed into R, G, and B channels. A: Visible light image (original image); B: R channel image; C: G channel image; D: B channel image.

Figure 2 Overview of the proposed framework. ASPP: Atrous Spatial Pyramid Pooling; MHSA: Multi-head self-attention; N: Normal; P2P: Protruding lesion larger than 1 cm.

Figure 3 Representative examples of invalid images and normal small bowel mucosa images. A: Bowel contents; B: Air bubbles; C: Overexposure; D: Oral cavity; E: Normal small bowel mucosa.

Figure 4 Trend plot of accuracy vs epoch.

Figure 5 Software-generated image representation of the heat map. A: P0Lk (lymphangiectasia); B: P0X (xanthoma); C: P1E (erosion); D: P1P (protruding lesion smaller than 1 cm); E: P2U (ulcer larger than 2 cm); F: P2P (protruding lesion larger than 1 cm); G: P2V (vascular lesion); H: B (bleeding).

Figure 6 Output of YOLO-V5. Boxes with different colors in the output image represent different bleeding risks; Green represents no bleeding risk; Yellow represents uncertain risk of bleeding; Magenta represents high bleeding risk; Red represents bleeding. Different numbers in the output image represent different lesion types. A: P0Lk (lymphangiectasia); B: P0Lz (lymphoid follicular hyperplasia); C: P0X (xanthoma); D: P1U (ulcer smaller than 2 cm); E: P1P (protruding lesion smaller than 1 cm); F: P2V (vascular lesion); G: P2U (ulcer larger than 2 cm); H: P2P (protruding lesion larger than 1 cm); I: Bleeding (B); J: P2P (protruding lesion larger than 1 cm), P2U (ulcer larger than 2 cm), and B. Decimal point represents probability.