Classification of Estrus Cycles in Rats by Using Deep Learning

Abstract

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.