It is important to find the polyps in a human system that helps to prevent
cancer during medical diagnosis. This research discusses using a dilated
convolution module along with a criss cross attention-based network to segment
polyps from the endoscopic images of the colon. To gather the context
information of all pixels in an image more efficiently, criss-cross attention
module has played a vital role. In order to extract maximum information from
dataset, data augmentation techniques are employed in the dataset. Rotations,
flips, scaling, and contrast along with varying learning rates were implemented
to make a better model. Global average pooling was applied over ResNet50 that
helped to store the important details of encoder. In our experiment, the
proposed architecture's performance was compared with existing models like
U-Net, DeepLabV3, PraNet. This architecture outperformed other models on the
subset of dataset which has irregular polyp shapes. The combination of dilated
convolution module, RCCA, and global average pooling was found to be effective
for irregular shapes. Our architecture demonstrates an enhancement, with an
average improvement of 3.75% across all metrics when compared to existing
models.

使用扩张卷积模块和交叉注意力网络对结肠内镜图像中的息肉进行分割，并结合数据增强技术和全局平均池化的方法，在处理不规则息肉形状时实现了明显的改进。