Cervical precancerous lesions classification using pre trained densely connected convolutional networks with colposcopy images
Abstract Colposcopy is currently a common medical technique for preventing cervical cancer. However, with the increase of the workload, screening by artificial vision has the problems of misdiagnosis and low diagnostic efficiency. Based on transfer learning, pre trained densely connected convolutional networks are used to propose a computer aided diagnosis (CAD) method for automatic classification of cervical precancerous. The proposed method is applied to determine CIN2 or higher level lesions in cervical images. In the present work, image data are initially prepossessing with ROI extraction and data augmentation. Then, parameters of all layers are fine tuning with pre trained DenseNet convolutional neural networks from two datasets (ImageNet and Kaggle) The impact of different training strategies on the model performance with limited training data is analyzed, including random initialization (RI) training from scratch, fine tuning (FT) pre trained model, different size of training data and K fold cross validation. Experimental results show that our method (FT) achieves an accuracy of 73.08% (AUC 0.75) in 600 test images. Compared with previous related work and clinicians, the performance of our approach can effective diagnosis CIN2+ and comparable with a senior physician, which proves the feasibility and promising of the proposed computer aided diagnostic method.
Biomed. Signal Process. Control.