Resumen
Endoscopy, a pervasive instrument for the diagnosis and treatment of hollow anatomical structures, conventionally necessitates the arduous manual scrutiny of seasoned medical experts. Nevertheless, the recent strides in deep learning technologies proffer novel avenues for research, endowing it with the potential for amplified robustness and precision, accompanied by the pledge of cost abatement in detection procedures, while simultaneously providing substantial assistance to clinical practitioners. Within this investigation, we usher in an innovative technique for the identification of anomalies in endoscopic imagery, christened as Context-enhanced Feature Fusion with Boundary-aware Convolution (GFFBAC). We employ the Context-enhanced Feature Fusion (CEFF) methodology, underpinned by Convolutional Neural Networks (CNNs), to establish equilibrium amidst the tiers of the feature pyramids. These intricately harnessed features are subsequently amalgamated into the Boundary-aware Convolution (BAC) module to reinforce both the faculties of localization and classification. A thorough exploration conducted across three disparate datasets elucidates that the proposition not only surpasses its contemporaries in object detection performance but also yields detection boxes of heightened precision.