Classification of Estrus Cycles in Rats by Using Deep Learning

The present study aims to accurately classify estrus cycle by using images of the uterus from female rats.Convolutional neural network-based deep learning techniques were utilized for the classification process.While the human menstrual cycle spans 28 days, in rats, it completes within 4-5 days.Female rats are particularly preferred in studies related to the female reproductive system due to being a model organism.In the study, sections stained with Hematoxylin and Eosin from the uterine tissue of female rats were examined under a light microscope, and their images were digitized.The obtained images were used to histologically classify the estrus cycles in rats.Following the examination, an artificial intelligence-based model was proposed for the classification of estrus cycles in rats using images obtained from uterine sections.The study classifies estrus cycles into four stages: proestrus, estrus, metestrus, and diestrus.In the proposed model, the classification success of sub-models belonging to the YOLOv5 algorithm, such as YOLOv5n, YOLOv5s, YOLOv5m was compared with histological results.The YOLOv5m model achieved an accuracy of 98.3%, precision of 99%, recall of 98%, and an F1-score of 98% in classification.By using the YOLOv5m architecture, a 98% accuracy in classifying estrus cycles was achieved, providing a robust deep learning approach for tissue analysis.The obtained results indicate that the proposed model can offer a second opinion support to expert pathologists in analyzing microscopic images.