One of the biggest hazards to cancer-related mortality globally is colorectal cancer, and improved patient outcomes are greatly in fluenced by early identi fication. Colonoscopy is a highly effective screening method, yet segmentation and detection remain challenging aspects due to the heterogeneity and variability of readers ’ interpretations of polyps.
In this work, we introduce a novel deep learning architecture for gastrointestinal polyp segmentation in the Kvasir-SEG dataset. Our method employs an encoder-decoder structure with a pre-trained ConvNeXt model as the encoder to learn multi-scale feature representations. The feature maps are passed through a ConvNeXt Block and then through a decoder network consisting of three decoder blocks.
Our key contribution is the employment of a cross-attention mechanism that creates shortcut connections between the decoder and encoder to maximize feature retention and reduce information loss. In addition, we introduce a Residual Transformer Block in the decoder that learns long-term dependency by using self-attention mechanisms and enhance feature representations. We evaluate our model on the.
Colorectal cancer is a signi ficant worldwide health concern with a high mortality rate and a complex course of development which typically starts in benign polyps. Polyps can become precancerous lesions, for which reason there is the utmost need for early discovery and intervention.
Effective screening and early polyp removal through colonoscopy are able to mitigate the severity of colorectal cancer, ultimately decreasing its incidence and mortality rates ( Torre et al., 2015 ;Song et al., 2020 ).
The American Cancer Society emphasizes the fact that normal screening can be able to pick up polyps before they turn into cancer, emphasizing the relevance of preventive intervention in the case of colorectal health management ( Torre et al., 2015 ). Risk factors associated with colorectal cancer be broadly divided into modi fiable and non-modi fiable factors. Obesity, physical inactivity, poor.
In this regard, arti ficial intelligence (AI) is appearing as a feasible solution to enhance polyp detection and segmentation. AI-driven systems are capable of analyzing colonoscopic images with higher accuracy and speed and therefore minimize the number of false negatives along with enhanced overall diagnostic outcomes ( Kang & Gwak, 2019 ; Singstad & Tzavara, 2021 ).
In addition, advances in segmentation techniques, such as the utilization of modi fied U-Net architectures and the use of attention mechanisms, have achieved greater performance in identifying polyp edges and thereby improving diagnostic accuracy ( Yang & Cui, 2024 ;Fu et al., 2022 ).
With the ever-growing sophistication of medical imaging, utilizing AI and machine learning for polyp segmentation is a basic step toward more effective approaches to the prevention of colorectal cancer. Endoscopy AI-based decision support systems have played very important roles in augmenting the ability of doctors to identify and segment polyps. The systems leverage sophisticated image.
inations. Existence of blind spots and human mistake can significantly impact the adenoma detection rate, which is an important measure of colonoscopy ef ficacy ( Li et al., 2023 ). According to reports in this study, the rate of missed polyps ranges from 22% to 28%, which makes it necessary to improve the detection processes. Polyp segmentation is very important.
It has a direct in fluence on the clinical management of colorectal cancer. Precise segmentation allows for better polyp parameter and morphology evaluation, which are very signi ficant during the planning of resection methods and follow-up treatment ( Su et al., 2021 ).
In this regard, arti ficial intelligence (AI) is appearing as a feasible solution to enhance polyp detection and segmentation. AI-driven systems are capable of analyzing colonoscopic images with higher accuracy and speed and therefore minimize the number of false negatives along with enhanced overall diagnostic outcomes ( Kang & Gwak, 2019 ; Singstad & Tzavara, 2021 ). In addition, advances in.
opy AI-based decision support systems have played very important roles in augmenting the ability of doctors to identify and segment polyps. The systems leverage sophisticated image processing and segmentation techniques, which are important to ensure that colonoscopy-based polyp detection is effective and ef ficient.
Their central aim is to help clinicians decide evidence-based for the segmentation of polyps, thereby addressing the problem of the polyp miss rate at the highest of 26% for small adenomas (Yeung et al., 2021 ).
With assisted deep learning architectures like U-Net and its variants, these systems are able to carry out real-time polyp detection and segmentation, offering a solid tool to gastroenterologists ( Ahmad et al., 2019 ;Chen, Urban & Baldi, 2022 ). For example, a study by Kang & Gwak (2019) has proven the application of ensemble models to polyp segmentation using methods such as fuzzy clustering.
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Physics in Medicine & Biology 68(20) :205002 DOI 10.1088/1361-6560/acf98f . Zhang Y, Yang G, Gong C, Zhang J, Wang S, Wang Y. 2024. Polyp segmentation with interference filtering and dynamic uncertainty mining. Physics in Medicine & Biology 69(7) :075016 DOI 10.1088/1361-6560/ad2b94 . Zhou Y, Li L. 2023. Hybrid spatial-channel attention and global-regional context aggregation feature for polyp.