Identification Of Imaging Features Of Diabetes Mellitus And Tuberculosis Based On YOLOv8x Model Combined With RepEca Network Structure

Abstract

Tuberculosis and diabetes mellitus are highly prevalent clinical conditions worldwide, and the mortality rate of tuberculosis is high; when diabetes mellitus is combined with tuberculosis, the interaction between the two can lead to a vicious cycle, posing a serious threat to the physical and mental health and life safety of patients, especially in developing regions where medical resources are scarce. In this paper, We trained several deep learning algorithm models based on YOLOv5, YOLOv8x, Faster R- CNN and Mask R-CNN with 4 types of lesion features commonly found in 1024 images, from which we selected the algorithm with the best automatic feature recognition effect and optimized the model to further improve the recognition efficiency. Combining the complexity of lesion features and experimental results, we propose a YOLOv8x model based on RepEca network structure and ESE attention mechanism, which is more effective than the original YOLOv8x in application, with an F1 metric value of 71.19%, and can better identify lesion features in images, assisting clinicians to improve the diagnostic accuracy and treatment effect.