# MAPPNet: A Multi-Scale Attention Pyramid Pooling Network for Dental Calculus Segmentation
## 5 Conclusions  
Recognizing dental calculus in oral cavity images poses numerous challenges due to obscured tissues, such as in the teeth and tongue, and the varied morphology of dental calculus itself. This study introduces a novel approach, MAPPNet, for dental calculus image segmentation. MAPPNet leverages an MSF module to perform feature extraction, progressively merging information to capture global context and generate a high-quality feature map. The PPM is also enhanced with different attention mechanisms to prioritize important \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**ISIC2018**} & \multicolumn{5}{c}{**Kvasir-SEG**} \\ \cline{2-10}  & **IoU (\%)** & **Sen (\%)** & **Spe (\%)** & **PPV (\%)** & **Acc (\%)** & **IoU (\%)** & **Sen (\%)** & **Spe (\%)** & **PPV (\%)** & **Acc (\%)** \\ \hline BiseNetV1 & 67.01 & 86.55 & 87.74 & 74.80 & 87.38 & 69.69 & 76.31 & 97.64 & 88.92 & 93.40 \\ CGNet & 67.78 & 76.96 & 94.30 & 85.04 & 89.17 & 84.94 & 94.15 & 97.31 & 89.67 & 96.68 \\ ICNet & 69.65 & 81.23 & 93.01 & 83.01 & 89.52 & 85.89 & 91.57 & 98.36 & 92.36 & 97.01 \\ MobileNetV3 & 73.42 & 78.84 & **96.89** & **91.43** & 91.55 & 78.19 & 80.76 & 99.19 & 96.10 & 95.53 \\ UNet & 74.46 & 84.17 & 94.52 & 86.59 & 91.45 & 83.48 & 88.18 & 98.60 & 93.99 & 96.53 \\ BiseNetV2 & 71.22 & 76.57 & 96.84 & 91.06 & 90.84 & 81.96 & 90.67 & 97.36 & 89.50 & 96.03 \\ ERFNet & 73.81 & 81.65 & 95.53 & 88.49 & 91.42 & 87.53 & 92.77 & 98.52 & 93.94 & 97.37 \\ Fastscnn & 72.39 & 80.23 & 95.44 & 88.11 & 90.94 & 83.32 & 91.48 & 97.57 & 90.33 & 96.36 \\ PSPNet & 67.32 & 73.66 & 96.04 & 88.66 & 89.41 & 88.19 & 91.70 & 99.02 & 95.85 & 97.56 \\ DMNet & 67.20 & 75.66 & 94.71 & 85.74 & 89.07 & 88.36 & 92.50 & 98.84 & 95.17 & 97.58 \\ DeepLabV3+ & 64.70 & 73.02 & 94.59 & 85.02 & 88.20 & 88.46 & 90.70 & 99.37 & **97.28** & 97.65 \\ HRNet & 70.30 & 76.36 & 96.38 & 89.87 & 90.45 & 87.34 & 90.37 & 99.14 & 96.29 & 97.40 \\ Fastfcn & 65.28 & 70.39 & 96.71 & 89.99 & 88.91 & 88.58 & 91.06 & 99.30 & 97.01 & 97.67 \\ ENCNet & 71.04 & 85.91 & 91.19 & 80.40 & 89.63 & 89.36 & 92.37 & 99.17 & 96.49 & 97.82 \\ CCNet & 73.36 & 83.82 & 94.00 & 85.46 & 90.99 & 89.73 & 92.41 & 99.26 & 96.87 & 97.90 \\ \hline TransUNet & 68.00 & 74.29 & 96.11 & 88.92 & 89.65 & 71.71 & 77.59 & 97.97 & 90.45 & 93.92 \\ Segmenter & 67.34 & 84.98 & 88.98 & 76.44 & 87.80 & 38.76 & 46.58 & 94.99 & 69.76 & 85.37 \\ Vit & 73.75 & 85.69 & 93.19 & 84.11 & 90.97 & 53.47 & 61.44 & 96.30 & 80.46 & 89.37 \\ Segformer & 73.13 & 81.38 & 95.25 & 87.82 & 91.05 & 67.80 & 71.79 & 98.54 & 92.43 & 93.23 \\ Swin & 73.75 & 85.20 & 93.47 & 84.58 & 91.02 & 66.40 & 71.46 & 98.11 & 90.36 & 92.82 \\ DPT & 71.34 & 83.74 & 92.69 & 82.81 & 90.04 & 76.14 & 82.65 & 97.88 & 90.62 & 94.85 \\ \hline MAPPNet & **76.48** & **87.08** & 94.17 & 86.27 & **92.07** & **91.38** & **94.16** & 99.25 & 96.87 & **98.23** \\ \hline \hline \end{tabular}
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Table 3: Comparison of different state-of-the-art methods for segmentation of skin lesions and gastrointestinal polyp tasks. Figure 9: Visual comparisons with different state-of-the-art methods on the gastrointestinal polyp segmentation task. feature information for accurate segmentation. Extensive ablation experiments demonstrate the efficacy of these modules in improving calculus segmentation performance. The most optimal combination of modules is determined for dental calculus image segmentation. Comparative experiments on some state-of-the-art segmentation networks further validate the superior performance of MAPPNet with multiple metrics. These results demonstrate the superiority of our proposed algorithm and make significant progress in improving the accuracy of edge information, showing that the proposed algorithm can accurately segment the calculus region and provide a reliable solution for future clinical diagnosis. However, the algorithm still faces certain challenges in accuracy when dealing with irregular lesion area segmentation.