KOA-YOLOv8s: an automatic diagnostic algorithm for X-ray images of knee osteoarthritis based on the improved YOLOv8s

Abstract Knee osteoarthritis (KOA) is a leading cause of limited mobility and physical disability among the elderly. Early detection and intervention are crucial for slowing disease progression and improving patients’ quality of life. This paper proposes an automated diagnostic algorithm based on an improved YOLOv8s model, named KOA-YOLOv8s, to enhance the detection performance for KOA. The algorithm introduces an Efficient Convolutional Attention Module (ECAM), which employs the Efficient Channel Attention (ECA) mechanism to enhance the channel attention capabilities of the Convolutional Block Attention Module (CBAM). This enhancement enables the network to focus more effectively on the critical information within images, thereby improving detection accuracy. We designed an Improved Large Selective Kernel Focal Modulation module (LSK-FM) based on the large selective kernel network (LSK) and Focal Modulation module to replace the traditional Simplified Spatial Pyramid Pooling-Fast (SPPF) module. LSK enables the Focal modulation module to better focus on the details of the target. Additionally, we propose a lightweight detection head, named tiny-Head, which is based on depthwise separable convolution (DSC). This head replaces the two standard convolutional layers in the original detection head, reducing the parameter count and computational complexity. KOA-YOLOv8s achieved a mean Average Precision (mAP) of 79.69 % on a dataset consisting of 9786 X-ray images for KOA, which represents a 3.11 % improvement over the previous iteration, with a detection speed of 78.81 frames per second (FPS). The experimental results demonstrate that the detection performance of the proposed algorithm is comparable to that of other state-of-the-art algorithms.