Cervical Precancerous Lesion Detection Based on Deep Learning of Colposcopy Images
With the rapid development of deep learning, automatic lesion detection is widely used in clinical screening. In this paper, we make use of convolutional neural network (CNN) algorithm to help medical experts detect cervical precancerous lesion during the colposcopic screening, especially
in the classification of cervical intraepithelial neoplasia (CIN). Firstly, the original image data is classified into six categories: normal, cervical cancer, mild (CIN1), moderate (CIN2), severe (CIN3) and cervicitis, which are further augmented to solve the problem of few samples of endoscopic
images and non-uniformity for each category. Then, a CNN-based model is built and trained for the multi-classification of the six categories, we have added some optimization algorithms to this CNN model to make the training parameters more effective. For the test dataset, the accuracy of the
proposed CNN model algorithm is 89.36%, and the area under the receiver operating characteristic (ROC) curve is 0.954. Among them, the accuracy is increased by 18%–32% compared with other traditional learning methods, which is 9%–20% higher than several commonly used deep learning
models. At the same number of iterations, the time consumption of proposed algorithm is only one quarter of other deep learning models. Our study has demonstrated that cervical colposcopic image classification based on artificial intelligence has high clinical applicability, and can facilitate
the early diagnosis of cervical cancer.
Keywords: Cervical Cancer; Colposcopy Images; Deep Learning; Image Classification; Lesion Detection
Document Type: Research Article
Affiliations: 1: School of Electronics and Communication, Guangdong Mechanical and Electrical Polytechnic, Guangzhou, 510550, China 2: Suzhou Institute of Advanced Technology, Chinese Academy of Sciences, Suzhou, 215123, China 3: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
Publication date: 01 May 2020
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content