InCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Gastrointestinal (GI) disease examination presents significant challenges to doctors due the intricate structure of human digestive system. Colonoscopy and wireless capsule endoscopy are most commonly used tools for GI examination. However, large amount data generated by these technologies requires expertise intervention identification, making manual analysis a very time-consuming task. Thus, development computer-assisted system is highly desirable assist clinical professionals in decisions low-cost effective way. In this paper, we introduce novel framework called InCoLoTransNet, designed polyp segmentation. The study based on transformer convolution-involution neural network, following encoder-decoder architecture. We employed vision encoder section focus global context, while decoder involves collaboration resampling features. Involution enhances model's ability adaptively capture spatial contextual information, convolution focuses local leading more accurate feature extraction. essential features captured passed through two skip connection pathways. CBAM module refines passes them block, leveraging attention mechanisms emphasize relevant information. Meanwhile, locality self-attention pass involution reinforcing regions. Experiments were conducted five public datasets: CVC-ClinicDB, CVC-ColonDB, Kvasir-SEG, Etis-LaribPolypDB, CVC-300. results obtained InCoLoTransNet optimal when compared with 15 state-of-the-art methods segmentation, achieving highest mean dice score 93% CVC-ColonDB 90% intersection over union, outperforming methods. Additionally, distinguishes itself terms segmentation generalization performance. It achieved high scores coefficient union unseen datasets as follows: 85% 79% 91% 87% CVC-300, 70% respectively.

Язык: Английский

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AFR: An image-aided diagnostic approach for ulcerative colitis

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 105, С. 107542 - 107542

Опубликована: Фев. 3, 2025

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HPANet: Hierarchical Path Aggregation Network with Pyramid Vision Transformers for Colorectal Polyp Segmentation

Algorithms, Год журнала: 2025, Номер 18(5), С. 281 - 281

Опубликована: Май 11, 2025

The automatic segmentation technique for colorectal polyps in colonoscopy is considered critical aiding physicians real-time lesion identification and minimizing diagnostic errors such as false positives missed lesions. Despite significant progress existing research, accurate of remains technically challenging due to persistent issues low contrast between mucosa, morphological heterogeneity, susceptibility imaging artifacts caused by bubbles the lumen poor lighting conditions. To address these limitations, this study proposed a novel pyramid vision transformer-based hierarchical path aggregation network (HPANet) polyp segmentation. Specifically, firstly, backward multi-scale feature fusion module (BMFM) was developed enhance ability processing with different scales. Secondly, forward noise reduction (FNRM) designed learn texture features upper lower layers reduce influence bubbles. Finally, order solve problem boundary ambiguity repeated up down sampling, refinement (BFRM) further refine boundary. compared several representative networks on five public datasets. Experimental results show that achieves better performance, especially Kvasir SEG dataset, where mDice mIoU coefficients reach 0.9204 0.8655.

Язык: Английский

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VGG16U-Net with Attention Based Semantic Segmentation of Gastrointestinal Abnormalities

Опубликована: Июль 23, 2024

Язык: Английский

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