SwinE-Net: hybrid deep learning approach to novel polyp segmentation using convolutional neural network and Swin Transformer

Prevention of colorectal cancer (CRC) by inspecting and removing colorectal polyps has become a global health priority because CRC is one of the most frequent cancers in the world. Although recent U-Net-based convolutional neural networks (CNNs) with deep feature representation and skip connections have shown to segment polyps effectively, U-Net-based approaches still have limitations in modeling explicit global contexts, due to the intrinsic nature locality of convolutional operations. To overcome these problems, this study proposes a novel deep learning model, SwinE-Net, for polyp segmentation that effectively combines a CNN-based EfficientNet and Vision Transformer (ViT)-based Swin Ttransformer. The main challenge is to conduct accurate and robust medical segmentation in maintaining global semantics without sacrificing low-level features of CNNs through Swin Transformer. First, the multidilation convolutional block generates refined feature maps to enhance feature discriminability for multilevel feature maps extracted from CNN and ViT. Then, the multifeature aggregation block creates intermediate side outputs from the refined polyp features for efficient training. Finally, the attentive deconvolutional network-based decoder upsamples the refined and combined feature maps to accurately segment colorectal polyps. We compared the proposed approach with previous state-of-the-art methods by evaluating various metrics using five public datasets (Kvasir, ClinicDB, ColonDB, ETIS, and EndoScene). The comparative evaluation, in particular, proved that the proposed approach showed much better performance in the unseen dataset, which shows the generalization and scalability in conducting polyp segmentation. Furthermore, an ablation study was performed to prove the novelty and advantage of the proposed network. The proposed approach outperformed previous studies.