The advancement of computer vision technologies has opened new avenues for the automated detection of viruses in medical slide images. This paper focuses on the implementation of YOLOv8, a state-of-the-art object detection algorithm, for the precise identification of various viruses in slide images. The targeted viruses include Adenovirus, Astrovirus, CCHF (Crimean-Congo Hemorrhagic Fever Virus), Covid19 (SARS-CoV-2), Cowpox, Ebola, Influenza, Lassa, Marburg, Nipah, Norovirus, Orf, Papilloma, Rift Valley Fever Virus, Rotavirus. The workflow encompasses the collection and annotation of a diverse dataset, preprocessing steps to ensure compatibility with YOLO, and the selection and training of the YOLOv8 model. Evaluation metrics such as precision, recall, and mean Average Precision (mAP) are employed to assess the model’s performance on a testing set. Furthermore, a user-friendly front end user interface (GUI) is created to facilitate seamless interaction and visualization of the detection results. The project aims to contribute to the automation of virus detection in medical imagery, offering a scalable solution that can assist healthcare professionals in diagnosing and understanding viral infections. The implementation and evaluation of the YOLOv8 model, coupled with the development of an intuitive GUI, provide a comprehensive framework for practical deployment and utilization in medical research and diagnostics. The insights gained from this project can potentially enhance the efficiency and accuracy of virus detection, thereby contributing to the broader field of medical image analysis and disease identification.