Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images

Abstract

Iron Deficiency Anaemia (IDA) is the most prevalent form of anaemia, affecting 24.8% of the global population. An examination of the complete blood count (CBC) is performed to determine general health and the presence of illnesses. Accurate and timely diagnosis of IDA is essential for proper treatment, yet traditional methods can be time-consuming and costly. This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. Two approaches were implemented: first, a ResNet50 model was used to classify PBS images as Normal or IDA; second, the YOLOv7 object detection model was employed to localize hypochromic microcytes within the images. The YOLOv7 model was tested on 17 images containing 425 instances of hypochromic microcytes and demonstrated superior performance, achieving a test mean Average Precision (mAP) of 89% with faster inference times than ResNet50. By providing localized detection of hypochromic microcytes, YOLOv7 enhances diagnostic accuracy and speed compared to image-level classification. This study highlights the potential of object detection models for improving automated anaemia diagnosis, with implications for faster and more cost-effective healthcare solutions.