# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Comparative Experiment with Other Convolutions
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & **Convolution** & **Dice** & **IoU** & **Accuracy** & **Specificity** & **Sensitivity** & **Precision** \\ \hline \multirow{4}{*}{HAM10000} & Conv2d & 0.9235 & 0.8604 & 0.9256 & 0.9350 & 0.9260 & 0.9237 \\  & AdaptiveConv2d & 0.9356 & 0.8811 & 0.9381 & 0.9532 & 0.9384 & 0.9360 \\  & Dilation & 0.9170 & 0.8518 & 0.9335 & 0.9332 & 0.9330 & 0.9068 \\  & Depthwise & 0.9289 & 0.8734 & 0.9453 & 0.9577 & 0.9345 & 0.9290 \\  & Group & 0.9108 & 0.8437 & 0.9168 & 0.9232 & 0.9146 & 0.9176 \\ \hline \multirow{4}{*}{Blood Cell} & Conv2d & 0.9759 & 0.9541 & 0.9790 & 0.9831 & 0.9767 & 0.9757 \\  & AdaptiveConv2d & 0.9806 & 0.9623 & 0.9841 & 0.9874 & 0.9811 & 0.9804 \\  & Dilation & 0.9728 & 0.9476 & 0.9761 & 0.9755 & 0.9750 & 0.9711 \\  & Depthwise & 0.9787 & 0.9587 & 0.9823 & 0.9571 & 0.9805 & 0.9773 \\  & Group & 0.9786 & 0.9584 & 0.9820 & 0.9839 & 0.9797 & 0.9777 \\ \hline \multirow{4}{*}{COVID-19} & Conv2d & 0.9106 & 0.8490 & 0.9900 & 0.9948 & 0.9173 & 0.9066 \\  & AdaptiveConv2d & 0.9177 & 0.8593 & 0.9926 & 0.9959 & 0.9239 & 0.9134 \\  & Dilation & 0.9124 & 0.8510 & 0.9895 & 0.9936 & 0.9262 & 0.9016 \\  & Depthwise & 0.9075 & 0.8443 & 0.9901 & 0.9943 & 0.9155 & 0.9026 \\  & Group & 0.8863 & 0.8151 & 0.9871 & 0.9919 & 0.9063 & 0.8712 \\ \hline \multirow{4}{*}{Kvasir} & Conv2d & 0.9015 & 0.8309 & 0.9488 & 0.9746 & 0.8984 & 0.9074 \\  & AdaptiveConv2d & 0.9172 & 0.8531 & 0.9360 & 0.9515 & 0.9206 & 0.9167 \\ \cline{1-1}  & Dilation & 0.8733 & 0.7901 & 0.9125 & 0.9448 & 0.8857 & 0.8730 \\ \cline{1-1}  & Depthwise & 0.8954 & 0.8230 & 0.9461 & 0.9705 & 0.8946 & 0.8987 \\ \cline{1-1}  & Group & 0.9013 & 0.8283 & 0.9486 & 0.9733 & 0.8951 & 0.9080 \\ \hline \hline \end{tabular} Note: The methods include different convolution strategies, such as standard convolution (Conv2d), AdaptiveConv2d, and others.  
\end{table} 
Table 3: Performance comparison of different convolution methods across four medical imaging datasets. Metrics such as Dice, IoU, accuracy, Specificity, Sensitivity, and Precision are used to evaluate segmentation quality for each method, including Conv2d, AdaptiveConv2d, Dilation, Depthwise, and Group Convolutions. AdaptiveConv2d consistently outperforms other methods, highlighting its effectiveness in enhancing segmentation performance across diverse datasets. can generally capture the target regions, but often result in blurry boundaries and miss small structures. As shown in Table 4, AdaptiveConv2d (Table 4, d) significantly enhances the accuracy of segmentation edges, especially for complex skin lesions, producing smoother and more precise results. In contrast, Hollow Convolution, Depthwise Separable Convolution, and Group Convolution perform well in general segmentation but fall short in handling finer details, resulting in slightly worse outcomes with more discontinuities and blurred boundaries. On the Blood Cell dataset, both traditional convolution and AdaptiveConv2d accurately identify all cells; however, Table 4 demonstrates that AdaptiveConv2d provides clearer segmentation boundaries, particularly in densely packed cell regions. Hollow (Table 4, l) and Group Convolutions (Table 4, n) are prone to introducing noise in such scenarios. On the COVID-19 dataset, Table 4 further highlights that AdaptiveConv2d stands out by minimizing information loss, whereas other methods, particularly Depthwise Separable Convolution, show considerable segmentation errors (Table 4, t). Similarly, on the Kvasir dataset, traditional (Table 4, x) and Depthwise Separable Convolutions (Table 4, aa) often produce \"gaps\" or \"breaks\" in the segmented regions, while Hollow (Table 4, z) and Group (Table 4, ab) Convolutions have a few minor errors. Only AdaptiveConv2d (Table 4, y) produces complete, clear, and noise-free segmentation masks consistently. design not only preserves the integrity of the input features but also enhances feature propagation, thereby boosting the network's expressiveness.