LAUSR

ABSTRACT In colposcopy-assisted diagnosis, the difference between the different lesion grades of colposcopic images is small, and the visual similarity is high. Therefore, it is a very challenging task to accurately diagnose cervical lesions through colposcopic images. This paper proposes a new risk assessment net of cervical lesions in colposcopic images (RACNet). The RACNet mainly consists of two parts. At first, the location and grade of lesions in different scales are detected through a multi-scale detection network. Then, these lesions are classified by designing a multi-branch convolutional neural network (CNN) to improve the performance of risk assessment. The RACNet was compared with the most advanced methods and colposcopists under the same condition. The experimental results show that the RACNet in this paper is superior to other methods, with an accuracy rate of 84.5%, which is 15% higher than the average level of colposcopists. It can provide clinicians with auxiliary diagnosis and reduce missed diagnosis and misdiagnosis.