Disease Detection Module for SBCE Images Using Modified YOLOv8

Small Bowel Capsule Endoscopy (SBCE) technology provides an intuitive and efficient means for detecting intestinal lesions. However, the generated videos and images, which can last for several hours, require a considerable amount of time for physicians to review. Utilizing deep learning and computer vision to automate lesion detection in SBCE is an effective approach to replace human experts. We propose a YOLO-SBCE model, derived from the YOLOv8 framework, to address the unique attributes of intestinal images, such as abnormal lesion morphology and fuzzy boundaries. The model introduces Channel-Spatial Omni-Dimensional Dynamic Convolution (CS-ODConv) to learn complementary attention across diverse dimensions within the kernel space, enabling the model to focus more on crucial features of the input. Additionally, an Alpha-CIoU loss function is employed to flexibly regulate the bounding box regression precision by adjusting the α value. To enhance the model's generalization capability and detection performance, the model incorporates a Multi-Head Context Aggregation (MHCA) network module. Finally, a lightweight Ghost module is incorporated to reduce parameters and computational complexity, thereby enhancing inference speed. Experimental results demonstrate that YOLO-SBCE outperforms existing classical visual models on the SEEAT and PASCAL VOC datasets.