Automatic Encoder Combined with Nasnet in Histopathologic Cancer Detection

Computer vision is a major branch of artificial intelligence algorithm. The algorithm of computer vision mainly consists of the processing of image and video, including image recognition and image detection etc. Practice has proved that computer vision is scientific and practical to a certain extent. In pace with the development of in-depth learning, computer vision has already been put to use well in all walks of life. However, it is still in exploring stage in the medical field, because the medical data is sensitive, which requires high accuracy of the algorithm. In this paper, images of PCam [1], [2] medical electron microscope are put to use for tumor detection, which is an task of image recognition and an automatic encoder is used to lower the dimensions of the data into low-dimensional vectors which are used as features in training. Then the vectors are added as features to the training, and the model is trained together with the original data set as the training features of NASnet. Because detection algorithms in the medical field pay more importance to the true positive rate and false positive rate. When the output is positive, it is necessary to be revalidated by SVM model trained by encoder. As a result, ROC curve is 0.98, which is 0.03 higher than Baseline.