Classification of Estrus Cycles in Rats by Using Deep Learning - Cecen, Seyma; Ceribasi, Songul; Erkus, Merve; Ozer, Ahmet Bedri; Tuncer, Taner; Cinar, Ahmet (Int Information & Engineering Technology Assoc, 2024)
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.
Keywords: deep learning histopathology estrus cycle; estrus staging pathological image; classification uterus YOLOv5