## 1 Introduction  
Colorectal cancer is a prevalent malignant tumor that affects the digestive tract, primarily occurring at the junction of the rectum and colon. Therefore, it is often collectively referred to as colorectal cancer with rectal cancer. In China, colorectal cancer ranks second in terms of the number of cases and fourth in terms of fatalities among all malignant tumors. The burden of colorectal cancer in China is substantial, with new cases accounting for approximately 30% of global cases, and East Asia contributing more than 75% of these cases. However, despite these alarming statistics, the situation for its prevention and control remains challenging, as reported by [1]. The high incidence and mortality rates of colorectal cancer place immense demands and stress on medical professionals, often leading to a decline in their well-being and an increased risk of errors during diagnosis and surgery. Therefore, the implementation of machine-assisted diagnosis and treatment could offer several benefits. It can enhance the diagnostic accuracy, improve patient survival rates, and significantly alleviate the pressure on medical practitioners. Furthermore, machine-assisted diagnosis can address the shortage of expert personnel, particularly in larger hospitals, resulting in a more efficient healthcare system. In clinical practice, colonoscopy serves as a valuable tool for providing crucial information regarding the location and appearance of colorectal polyps. However, determining the conditions of colorectal polyps based solely on a colonoscopy can be challenging. Our analysis of related datasets of polyps revealed three challenges: First, each pathological polyp encompasses multiple categories, even within the same polyp type; there is no uniform appearance. The lack of uniform texture information greatly increases the difficulty of segmentation, which can very easily lead to the wrong detection of polyps. Second, multiple polyps are present in the same colonoscopy image. In this particular case, the segmentation network often focuses on one of the polyps, resulting in missed detection. Finally, due to the tissue-based nature of the polyps, there is often no clear boundary between the polyps and their surrounding mucosal tissue, which is a very big challenge for polyp segmentation, and most segmentation networks do not handle this challenge well. To tackle these challenges, this paper introduces a novel deep neural network designed for precise polyp segmentation named the boundary constraint multi-level feature aggregation framework (BMFA-Net). Our research was grounded in three key considerations: First, [PERSON] et al. [2] pointed out that convolutional neural networks pay more attention to texture extraction. Therefore, we designed a five-layer convolutional network with Res2 Net as the basic skeleton. In contrast to CaraNet [3], which relies on single-level feature utilization, we drew inspiration from the findings from CSPNet [4], which reveal that multi-level feature aggregation yields features with superior discrimination and robustness. This, in turn, enhances the classification, detection, and identification performance while acquiring more accurate and dependable spatial information. It contributes to improving the overall model accuracy. Therefore, we designed a framework to aggregate the information of the previous level and the information of the latter level so that the network can learn the information between multiple levels and the network focuses on both the overall information and the local information to improve the overall accuracy of the model. Second, different from the channel and feature pyramid CFP module (CFPNet [5]) introduced by CaraNet, we exploited the power of the atrous convolution operators to extend the receptive fields. This extension allowed for the integration of global semantic information without sacrificing the resolution or coverage.