An efficient framework for real-time colorectal polyp detection using local outlier factor-based preprocessing and YOLOv11n.

Timely and accurate detection of colorectal polyps plays an important role in the prevention and early diagnosis of colorectal cancer. Despite the advancement of deep learning-based methods, automatic polyp detection remains a challenging problem due to factors such as the small size of polyps, apparent similarity of polyps to surrounding tissue, variable quality of colonoscopy images, and the presence of noisy samples in the training data. In this study, a lightweight, fast, and robust framework for colorectal polyp detection was proposed that combines Local Outlier Factor (LOF)-based preprocessing with the YOLOv11n object detection model. In this study, five public datasets were used, including CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG, ETIS, and EndoScene. Since these datasets originally contained segmentation labels, the binary masks of polyps were converted into bounding boxes to be used for training the object detection model. To improve the quality of the training data, LOF with 30 neighbors and a contamination rate of 5% was applied only to the training set to remove outliers and potentially noisy samples, while the validation and test data were left unchanged. Then, the YOLOv11n model was trained using the cleaned data and its performance was evaluated using five-fold cross-validation. The results showed that the proposed LOF-YOLOv11n framework achieved Precision of 94.73%, Recall of 91.46%, F1-score of 93.28%, [email protected] of 96.54%, and [email protected]:0.95 of 78.01%. Also, the model showed an average speed of 56.1 frames per second, indicating its potential for image-level real-time inference under the evaluated experimental conditions. Supplementary analyses and ablation studies showed that LOF-based preprocessing can provide descriptive and limited improvements in the stability of the learning process and model performance, without biasing the model evaluation by removing difficult examples from the test set. Overall, the results suggest that combining training-data quality improvement with a lightweight YOLOv11n detector may provide a promising image-level framework for computer-aided colorectal polyp detection, although further validation on independent clinical and video-based datasets is required before clinical deployment.