Enhancing COVID-19 Detection with YOLOv5 Object Detection Algorithm in Chest X-ray Images

In the ongoing fight against COVID-19, accurate diagnostic tools are crucial. This study focuses on the detection of COVID-19, primarily by analyzing Chest X-ray images to identify and understand the disease. We introduce a new approach that utilizes the YOLOv5 (You Only Look Once) object detection algorithm. YOLOv5 is known for its ability to quickly identify objects, and we have fine-tuned it to perform effectively with X-ray images. The result is a model that excels at identifying intricate COVID-19-related patterns in X-ray images, achieving a high accuracy score of 98%. By carefully adjusting and training the model with specific data, our method achieves even higher accuracy. This modification makes YOLOv5 a powerful tool for automating the detection of COVID-19, offering hope for early detection and timely action in the ongoing global fight against the pandemic.