DMID-ESD: A Benchmark Darkfield Microscopy Image Dataset for Erythrocytes and Spirochaete Detection and Classification

Since the analysis of erythrocytes and spirochaetes is highly relevant to human health, their automated detection is of significant importance in both medical and computer vision research. However, publicly available datasets in this domain remain scarce. To address this gap, we present the Darkfield Microscopy Image Dataset for Erythrocytes and Spirochaete Detection (DMID-ESD), which consists of 11,794 fully annotated images with labels containing categorical and localization information. We perform comprehensive benchmarking experiments on DMID-ESD to evaluate its utility in tasks such as image classification, object detection, and feature extraction. The results demonstrate that the dataset serves as an effective benchmark for method evaluation. The DMID-ESD dataset is freely available for non-commercial use at: https://figshare.com/articles/dataset/DMID-ESD_zip/22179311.