# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Comparative Experiment with Other Convolutions  
Table 3 shows that only 5 of the 120 experimental indicators did not demonstrate improvement. The remaining indicators all improved to some extent, resulting in an overall improvement rate of 95.83%. Notably, the Dice and IoU metrics, which are primarily used to evaluate segmentation performance, achieved a 100% improvement rate. On the HAM10000 dataset, AdaptiveConv2d significantly outperforms other convolution methods. Specifically, its Dice coefficient reaches 0.9356, which is 1.21% higher than that of the standard convolution (increasing from 0.9235 to 0.9356). Compared to Hollow Convolution (Dice: 0.9170), Depthwise Separable Convolution (Dice: 0.9289), and Group Convolution (Dice: 0.9108), AdaptiveConv2d improves the Dice coefficient by 1.86%, 0.67%, and 2.48%, respectively. AdaptiveConv2d also achieves the highest values for IoU, accuracy, Specificity, Sensitivity, and Precision, highlighting its superior capability to capture the details and edge features of skin lesion regions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Image/Mask** & **UNet** & **UNet++** & **nn-UNet** & **Trans UNet** & **Attention-UNet** \\ \hline COVID-19 & & & & & & \\ \hline Kvasir & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._AdaptiveConv2d also demonstrates notable performance on the Blood Cell dataset, achieving a Dice coefficient of 0.9806, which is 0.47% higher than that of the standard convolution (increasing from 0.9759 to 0.9806). Compared to Hollow Convolution (Dice: 0.9728), Depthwise Separable Convolution (Dice: 0.9787), and Group Convolution (Dice: 0.9786), AdaptiveConv2d improves the Dice coefficient by 0.78%, 0.19%, and 0.20%, respectively. The ability of AdaptiveConv2d to achieve performance improvements despite high baseline performance emphasizes its advantage in fine feature extraction and precise segmentation. On the COVID-19 dataset, AdaptiveConv2d performs exceptionally well in handling complex backgrounds and uneven lesion areas. Its Dice coefficient reaches 0.9177, which is 0.71% higher than that of the standard convolution (increasing from 0.9106 to 0.9177). Compared to Hollow Convolution (Dice: 0.9124), Depthwise Separable Convolution (Dice: 0.9075), and Group Convolution (Dice: 0.8863), AdaptiveConv2d improves the Dice coefficient by 0.53%, 1.02%, and 3.14%, respectively. The improvement in other evaluation metrics further confirms the effectiveness of AdaptiveConv2d in accurately identifying regions of lung infection. Similarly, AdaptiveConv2d performs strongly on the Kvasir dataset, achieving a Dice coefficient of 0.9172, which is 1.57% higher than that of the standard convolution (increasing from 0.9015 to 0.9172). Compared to Hollow Convolution (Dice: 0.8733), Depthwise Separable Convolution (Dice: 0.8954), and Group Convolution (Dice: 0.9013), AdaptiveConv2d improves the Dice coefficient by 4.39%, 2.18%, and 1.59%, respectively. These substantial improvements demonstrate AdaptiveConv2d's strong capability to capture the complex textures and fine structures present in endoscopic images, thereby enhancing segmentation accuracy and reliability. The differences in performance among various convolution operations are particularly evident in the HAM10000 dataset. Traditional convolution operations (Conv) (Table 4, c)