Improved Colorectal Polyp Segmentation Using Enhanced MA-NET and Modified Mix-ViT Transformer

ELKarazle, Khaled; Raman, Valliappan; Then, Patrick; Chua, Caslon

doi:10.1109/access.2023.3291783

Cited by 4 publications

(1 citation statement)

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“…During the colonoscopy, a colonoscope, a flexible tube equipped with a tiny camera at its tip, is inserted into the colon. Polyps can be removed during the procedure if necessary [5]. However, the colonoscopy examination is time-consuming and highly labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Chen,

Ahmad

2023

IEEE Access

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Colorectal cancer has been one of the leading causes of mortality over the past decade, and colorectal polyps are the leading cause of this disease. Conventional polyp detection techniques are insufficient for proper detection; thus, an efficient detection method is indispensable. In this study, we collected colorectal images from a hospital in Taiwan, annotated the ground truth of polyp locations, and integrated them with a public dataset to create a colonoscopy dataset. Data augmentation techniques are further used to increase the training dataset's diversity to improve the models' detection performance. By developing the comparison system based on the recent state-of-the-art methods (i.e., FasterRCNN, SSD, YOLOv3, and YOLOv4), we compared the measurement metrics and statistically analyzed the performance of the models to identify the significant statistical difference in models' performance. Moreover, we developed and integrated error handling mechanism with each model to discard the false and null predictions. Finally, our model comparison system selects and proposes the best performing deep learning model to detect and classify colorectal polyps. We expect that the proposed model will accurately locate and classify different types of polyps. Eventually, this approach will ensure a valuable medical aid model. INDEX TERMSColorectal cancer (CRC), colorectal polyps, polyp detection, deep learning, data augmentation, error handling.

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Section: Introductionmentioning

confidence: 99%

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Chen,

Ahmad

2023

IEEE Access

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Rethinking encoder-decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation

Iqbal,

Ahmed,

Usman

et al. 2024

Engineering Applications of Artificial Intelligence

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Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI

Ahamed,

Islam,

Nahiduzzaman

et al. 2024

IEEE Access

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Detecting colorectal polyps promptly and accurately is crucial in preventing the progression of colorectal cancer. These polyps cause severe conditions in the colon or rectum, presenting a significant diagnostic challenge. Traditional manual detection through medical imaging is not only bulky and prone to errors but also incurs substantial costs, requiring expert endoscopist. Inefficient detection and treatment can lead to critical health complications. Addressing these issues, we extensively employed various configurations of the state-of-the-art YOLOv8 (n-nano, s-small, m-medium, l-large, and x-extra-large) models for effective polyp localization. Complementing this, we proposed a novel TR-SE-Net model for segmentation, integrating Squeeze-and-Excite Networks (SE-Net) to elevate performance and real-time processing capabilities. The Kvasir-SEG dataset is utilized for training and testing models, supplemented by external validation CVC-ClinicDB, PolypGen, ETIS-LaribPolypDB, EDD 2020, and BKAI-IGH to confirm their efficacy in processing unseen, real-time data. This study delves into the interpretability of these models using explainable AI (XAI), such as eigen visualization for localization and heatmap analysis for segmentation. This exploration provides deeper insights into the decision-making processes of the models, thereby enhancing their reliability. Notably, the YOLOv8m model showcased remarkable prediction speed (approximately 16.61 ms) and excelled in precision (0.946), recall (0.771), F1-score (0.85), mAP50 (0.886), and mAP50-95 (0.695), catering to diverse clinical scenarios. The TR-SE-Net demonstrated significant improvements in segmentation performances, including DSC (0.8754), F2-score (0.8786), precision (0.9027), recall (0.8879), accuracy (0.9647), competitive mIoU (0.7961), FPS (54), parameters (27.27 million), and flops (10.59 GMac). Furthermore, A graphical Computer Aided Diagnosis (CAD) system developed utilizing both models can substantially reduce the miss rate because segmentation will assist in polyp detection or vice versa if localization fails. Conclusively, integrating these advanced computer-aided methods substantially enhances colonoscopy procedures by mitigating the risks of colorectal cancer.

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Improved Colorectal Polyp Segmentation Using Enhanced MA-NET and Modified Mix-ViT Transformer

Cited by 4 publications

References 50 publications

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Rethinking encoder-decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation

Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI

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