Comparative Analysis of yOLO Architectures for Automated Detection of Liver Disease in Histopathological Images

Liver disease is one of the major health problems worldwide and usually leads to serious complications if not diagnosed accurately and in time. Effective detection and classification of liver pathology at early stages is crucial, in which histopathologic examination of liver tissue plays a key role. However, manual analysis of histopathological images is easily affected by inter-observer variability. Recent advances in deep learning, on the other hand, have introduced methods to significantly improve the accuracy and efficiency of image-based diagnosis. This study focuses on the application of the You Only Look Once (YOLO) object detection model, specifically YOLOv4, v5, v7, v8, and v9, for automated detection of liver diseases from stained microscopic liver slices. We perform a comprehensive comparative analysis to evaluate the detection accuracy of these models across four common liver conditions: ballooning, fibrosis, inflammation, and steatosis. The results of the study show that the latest versions, in particular YOLOv9, show significant improvements in accuracy and computational efficiency compared to other versions. In this paper, the performance of each model is evaluated in detail, and our results emphasize the potential of the advanced YOLO architecture to enhance medical diagnostics by facilitating faster and more reliable detection of liver disease.