# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 2 Materials and Methods
### Datasets  
To evaluate the effectiveness and applicability of AdaptiveConv2d, we utilized four publicly available medical image datasets, each accessed via Kaggle. Five-fold cross-validation was conducted to ensure robustness in the results. The details of the datasets are as follows:  
1. Skin cancer: HAM10000: This is a public dataset consisting of 10,000 pairs of images and masks of skin cancer cases. The cases include a representative collection of all important diagnostic categories in the field of pigmented lesions, including actinic keratosis and intraepithelial carcinoma/Bowen's disease (AKIEC), basal cell carcinoma (BCC), benign keratosis-like lesions (solar lentigo/seborrheic keratosis and lichen planus-like keratosis), and vascular lesions (hemangioma, angiolipoma, pyrogenic granuloma and hemorrhage, and VASC). Each image is 600 \(\times\) 450 pixels in size ([[https://www.kaggle.com/datasets/suraghuvalewala/ham1000-segmentation-and-classification](https://www.kaggle.com/datasets/suraghuvalewala/ham1000-segmentation-and-classification)]([https://www.kaggle.com/datasets/suraghuvalewala/ham1000-segmentation-and-classification](https://www.kaggle.com/datasets/suraghuvalewala/ham1000-segmentation-and-classification)) (accessed on 15 February 2025));
2. Blood Cell Segmentation Dataset: This dataset is a public dataset consisting of 1328 pairs of original red blood cell images and mask images, each with a size of  
Figure 5: Dynamic residual connection with global context integration.  
1600 \(\times\) 1200 pixels [39] ([[https://www.kaggle.com/datasets/jeetblahiri/bccd-dataset-with-mask](https://www.kaggle.com/datasets/jeetblahiri/bccd-dataset-with-mask)]([https://www.kaggle.com/datasets/jeetblahiri/bccd-dataset-with-mask](https://www.kaggle.com/datasets/jeetblahiri/bccd-dataset-with-mask)) (accessed on 15 February 2025));
3. COVID-19 CT scan lesion segmentation dataset: This dataset is a public dataset that collects data from seven public COVID-19 datasets to create a large lung CT scan dataset. Three of these datasets have shared COVID-19 lesion masks. This dataset combines the COVID-19 lesion masks and their corresponding frames from these three public datasets, for a total of 2729 image-mask pairs. All different types of lesions are mapped to white to ensure consistency across different datasets. Each image is 512 \(\times\) 512 pixels in size ([[https://www.kaggle.com/datasets/maedemaftouni/covid1](https://www.kaggle.com/datasets/maedemaftouni/covid1)]([https://www.kaggle.com/datasets/maedemaftouni/covid1](https://www.kaggle.com/datasets/maedemaftouni/covid1)) 9-ct-scan-lesion-segmentation-dataset (accessed on 15 February 2025));
4. The Kvasir Dataset: This dataset is publicly available. The KVASIR dataset was released as part of the MediaEval proposed medical multimedia challenge. The dataset is based on images obtained from the digestive tract via endoscopic surgery. The dataset consists of images annotated and validated by doctors and contains 8 different categories. The categories are based on three anatomical landmarks (Z-line, pylorus, and cecum), three pathological findings (esophagitis, polyp, and ulcerative colitis), and two other categories related to the polypectomy procedure (dyeing and lifting the polyp, and dyeing the resection margin). Overall, the dataset consists of 8000 pairs of endoscopic images and masks, with each image measuring 622 \(\times\) 529 pixels ([[https://www.kaggle.com/datasets/abdallahwagih/kvasir-dataset-for-classification-and-segmentation](https://www.kaggle.com/datasets/abdallahwagih/kvasir-dataset-for-classification-and-segmentation)]([https://www.kaggle.com/datasets/abdallahwagih/kvasir-dataset-for-classification-and-segmentation](https://www.kaggle.com/datasets/abdallahwagih/kvasir-dataset-for-classification-and-segmentation)) (accessed on 15 February 2025)).