An improved algorithm for full-mouth lesion detection based on YOLOv8

In medical imaging detection of oral Cone Beam Computed Tomography (CBCT), there exist tiny lesions that are challenging to detect with low accuracy. The existing detection models are relatively complex. To address this, this paper presents a dual-stage YOLO detection method improved based on YOLOv8. Specifically, we first reconstruct the backbone network based on MobileNetV3 to enhance computational speed and efficiency. Second, we improve detection accuracy from three aspects: we design a composite feature fusion network to enhance the model’s feature extraction capability, addressing the issue of decreased detection accuracy for small lesions due to the loss of shallow information during the fusion process; we further combine spatial and channel information to design the C2f-SCSA module, which delves deeper into the lesion information. To tackle the problem of limited types and insufficient samples of lesions in existing CBCT images, our team collaborated with a professional dental hospital to establish a high-quality dataset, which includes 15 types of lesions and over 2000 accurately labeled oral CBCT images, providing solid data support for model training. Experimental results indicate that the improved method enhances the accuracy of the original algorithm by 3.5 percentage points, increases the recall rate by 4.7 percentage points, and raises the mean Average Precision (mAP) by 3.3 percentage points, a computational load of only 7.6 GFLOPs. This demonstrates a significant advantage in intelligent diagnosis of full-mouth lesions while improving accuracy and reducing computational load.