Modified YOLO network model for metaphase cell detection in antinuclear antibody images

The antinuclear antibody (ANA) test plays the role of screening, diagnosis, treatment and prognosis for autoimmune diseases. However, the ANA test is time-consuming, and strongly depends on the interpretation of experienced medical examiners. With the rise of artificial intelligence, a lot of convolutional neural networks (CNN) have been used to recognize the features of ANA images, such as GoogLeNet, ResNet, and MobileNet. By extracting the disease-related features from the entire ANA image, these models achieve acceptable accuracy, but none of them can replace the manual ANA test completely. This is because they ignore the key features of ANA images. Accordingly, this paper aims to design an object detection model that can directly identify the key features of ANA images, i.e., the Metaphase cells. This process is critical in the manual ANA test performed by the medical examiners. Based on the YOLO model, our design first applies various image preprocessing and data augmentation techniques to enhance the critical features in the training data. We modify the original YOLO model, in which the residual block is replaced with SENet module to improve the classification performance by emphasizing critical features and reducing the influence of low-level features. Moreover, we use the different activation function to prevent the modified model from partial judgment. The experimental results show that our design can effectively improve the performance of ANA metaphase identification. Compared to the related CNN models, our design can improve F1-score and mAP by range 5%-14% and 11%-13%, respectively. In particular, the Kappa value of our design is 0.82 that is almost perfect agreement with the ANA experts.