A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor

Yu, Haiyun; Luo, Shaoze; Ji, Junyu; Wang, Zhiqiang; Zhi, Wenxue; Mo, Na; Zhong, Pingping; He, Chunyan; Wan, Tao; Jin, Yulan

doi:10.3390/life13010003

Cited by 4 publications

(2 citation statements)

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“…Our study, employing the YOLOv5 model, achieved a high accuracy rate of 98.3%, showcasing the success of the model compared to similar studies. Similarly, in the study by Yu et al [14] in 2023, focusing on uterine smooth muscle tissue, the YOLOv5 model was preferred. The study, conducted on 3-class 224×224-sized images, achieved a success rate of 92%.…”

Section: Discussion and Experimental Resultsmentioning

confidence: 99%

“…According to experimental results, the proposed model was stated to outperform CenterNet, YOLOv5, and Faster R-CNN algorithms in certain aspects such as time consumption and high recognition accuracy. Yu et al [14] conducted research on pathological whole-slide images of uterine smooth muscle tumors. While the ResNet model was used as a classification network for cytologic atypia and necrosis, the YOLOv5 model was employed for mitotic count detection.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Estrus Cycles in Rats by Using Deep Learning

Çeçen,

Çeribaşı,

Erkuş

et al. 2024

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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.

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Section: Discussion and Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%