This project utilizes state-of-the-art deep learning methods, YOLOv8 and DETR, for the detection of diseases in chest X-ray images. The combination of these two powerful frameworks allows for accurate and efficient identification of abnormalities in medical images.
Medical image analysis plays a crucial role in early disease diagnosis. This project focuses on leveraging YOLOv8 and DETR, two popular deep learning frameworks, to detect diseases in chest X-ray images. The models are trained on large datasets to recognize various abnormalities, providing a valuable tool for healthcare professionals.
YOLOv8, short for "You Only Look Once version 8," is an efficient and accurate object detection model. It divides an image into a grid and predicts bounding boxes and class probabilities for each grid cell.
DETR is an open-source framework for object detection. It supports a variety of pre-trained models and provides flexibility for customizing models. In this project, DETR is used for its versatility and robustness.