Hemoglobin based anemia detection

Anemia is a prevalent global health issue characterized by a deficiency of red blood cells or hemoglobin, impairing oxygen transport in the body. Early detection is crucial to prevent complications and manage treatment effectively. This project proposes a deep learning-based approach for the detection of anemia using YOLOv10, a cutting-edge object detection algorithm. The focus is on identifying anemic and non-anemic conditions based on a specialized dataset, which includes annotated images of blood samples, providing the necessary input for the model to classify them. The dataset for this task is obtained from the Anemia Detection Dataset on Roboflow, which includes labeled images for both anemic and non-anemic classes.

The YOLOv10 model is chosen for its high performance in object detection tasks due to its speed and accuracy. The model will be trained to recognize specific features indicative of anemia, such as abnormal cell shapes or colors in blood samples, and then classify these images as either anemic or non-anemic. The model’s performance will be evaluated using standard metrics like accuracy, precision, recall, and F1-score.

The goal of this project is to create an automated system that can assist healthcare professionals in diagnosing anemia faster and more accurately. This system can be integrated into diagnostic tools to reduce human error, improve patient outcomes, and facilitate efficient healthcare delivery.

Keywords: Anemia detection, YOLOv10, object detection, blood sample classification, deep learning, hemoglobin deficiency, automated diagnosis, medical imaging, healthcare automation, AI in healthcare.