# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Comparison Experiment with SOTA Deep Learning Methods
For the more complex COVID-19 dataset, the AdaptiveConv2d-enhanced models demonstrated robust performance in handling intricate backgrounds and irregular lesions. The Dice coefficient of the UNet-AD model improved from 0.9106 to 0.9177, representing a 0.71% improvement, and the IoU increased from 0.8490 to 0.8593. Similarly, the Dice coefficients of nn-UNet-AD and Attention-UNet-AD improved by 1.11% and 1.32%, respectively. These results underscore AdaptiveConv2d's advantage in enhancing the model's capacity to recognize complex structures and subtle edge areas, thereby achieving more accurate and reliable segmentation of lung infection regions. On the Kvasir dataset, the models faced challenges such as lighting variations, noise, and complex backgrounds. Despite these challenges, the UNet-AD model achieved significant performance gains, with the Dice coefficient increasing from 0.9015 to 0.9172, an enhancement of 1.57%, and the IoU improving from 0.8309 to 0.8531. While Specificity decreased slightly, improvements in Sensitivity and accuracy suggest that the model became more effective in correctly identifying lesion areas. AdaptiveConv2d enhanced the model's focus on the target region in complex scenes, reducing interference from background noise. The overall performance improvement provided by AdaptiveConv2d is evident across all datasets, with the method consistently yielding better segmentation results. These consistent improvements are attributed to the following:  
1. **Enhanced Feature Extraction:** By introducing an attention mechanism, AdaptiveConv2d improved the model's ability to capture critical features, especially in medical images with rich edge and detail information. 2. **Improved Segmentation Accuracy:** Performance gains in key metrics such as the Dice coefficient and IoU ranged from 0.47% to 3.50%, highlighting the effectiveness of AdaptiveConv2d in enhancing segmentation accuracy. 3. **Adaptability to Diverse Datasets:** AdaptiveConv2d showed versatility across different medical imaging domains, including skin lesions, blood cells, lung CT scans, and endoscopic images, demonstrating its adaptability and robustness. Additionally, AdaptiveConv2d-enhanced models exhibited improvements in both Specificity and Sensitivity, resulting in a more balanced ability to correctly identify both positive and negative examples. This is particularly important in medical diagnostics, as it reduces the incidence of false positives and false negatives, which can have critical implications in clinical decision-making. As shown in Table 2, AdaptiveConv2d led to more refined handling of segmentation edges on the HAM10000 dataset. Segmentation masks produced by models with standard convolutions often exhibited blurry and smooth edges, neglecting important details and gradient regions. On the Blood Cell dataset, AdaptiveConv2d demonstrated its ability to enhance the segmentation of small target structures. Models without AdaptiveConv2d tended to miss small cells or blur details when processing densely packed cell regions, particularly around cell edges. In contrast, models incorporating AdaptiveConv2d better distinguished each small cell, producing clearer and more complete segmentation results, thereby significantly reducing instances of misdiagnosis or missed diagnosis. This highlights AdaptiveConv2d's strength in improving fine-grained details when segmenting small objects. On the COVID-19 dataset, segmentation results without AdaptiveConv2d showed significant information loss, particularly in the upper-right corner of the segmentation images produced by UNet ((Table 2, z) and (Table 2, af)) and UNet++ ((Table 2, aa) and (Table 2, ag)), where information loss was pronounced. Additionally, UNet++ failed to segment the left lung contour, indicating limitations in feature extraction. AdaptiveConv2d's improvements in image feature extraction enabled the segmentation network to more accurately delineate target regions, especially at fuzzy tissue boundaries and in complex anatomical structures.