Cancer is a disease that occurs as a result of the uncontrolled division and proliferation of cells. Colon cancer is one of the most common types of cancer in the world. Polyps that can be seen in the large intestine can cause cancer if not removed with early intervention. Deep learning and image segmentation techniques are used to minimize the number of polyps that goes unnoticed by the experts during these interventions. Although these techniques perform well in terms of accuracy, they require too many parameters. We propose a new model to address this problem. Our proposed model requires fewer parameters as well as outperforms the state-of-the-art models. We use EfficientNetB0 for the encoder part, as it performs well in various tasks while requiring fewer parameters. We use partial decoder, which is used to reduce the number of parameters while achieving high accuracy in segmentation. Since polyps have variable appearances and sizes, we use an asymmetric convolution block instead of a classic convolution block. Then, we weight each feature map using a squeeze and excitation block to improve our segmentation results. We used different splits of Kvasir and CVC-ClinicDB datasets for training, validation, and testing, while we use CVC- ColonDB, ETIS, and Endoscene datasets for testing. Our model outperforms state-of-art models with a Dice metric of %71.8 on the ColonDB test dataset, %89.3 on the EndoScene test dataset, and %74.8 on the ETIS test dataset while requiring fewer parameters. Our model requires 2.626.337 parameters in total while the closest model in the state-of-the-art is U-Net++ with 9.042.177 parameters.
翻译:癌症是一种由细胞不受控制地分裂和扩散而导致的疾病。 Colon 癌症是世界上最常见的癌症类型之一。 在大肠胃中可以看到的聚合物,如果不通过早期干预来移除,就会导致癌症。 深度学习和图像分解技术被用于最大限度地减少在这些干预过程中被专家忽视的聚虫数量。 虽然这些技术在准确性方面表现良好, 但是它们需要过多的参数。 我们建议了一个新的模型来解决这个问题。 我们提议的模型需要的参数较少, 并且超过了最先进的模型。 我们使用有效NetB0 来计算编码值部分, 因为它在各种任务中表现良好, 而需要更少的参数。 我们使用部分解析和图像分解技术, 用来减少参数数量, 同时实现高度的分解分解。 由于这些技术在外观和大小方面效果方面表现优异, 我们使用一个不对称的曲线块块来测量每颗粒图, 用一个压缩和推力块来改进我们的分解结果。 我们用不同分裂的 Kvasier 和 CVC 测试模型, 用EVC 测试S 数据测试模型来测试我们C。