Medical Image Authentication Method Based on the Wavelet Packet and Energy Entropy.

The transmission of digital medical information is affected by data compression, noise, scaling, labeling, and other factors. At the same time, medical data may be illegally copied and maliciously tampered with without authorization. Therefore, the copyright protection and integrity authentication of medical information are worthy of attention. In this paper, based on the wavelet packet and energy entropy, a new method of medical image authentication is designed. The proposed method uses the sliding window to measure the energy of the detail information. In the time-frequency data distribution, the local details of the data are mined. The complexity of energy is quantitatively described to highlight the valuable information. Based on the energy weight, the local energy entropy is constructed and normalized. The adjusted entropy value is used as the feature vector of the authentication information. A series of experiments show that the authentication method has good robustness against shearing attacks, median filtering, contrast enhancement, brightness enhancement, salt-and-pepper noise, Gaussian noise, multiplicative noise, image rotation, scaling attacks, sharpening, JPEG compression, and other attacks.

Video Image Moving Target Recognition Method Based on Generated Countermeasure Network.

In order to improve the accuracy of video image moving target recognition and shorten the recognition time, a video image moving target recognition method based on a generation countermeasure network is proposed. Firstly, the image sensor is used to collect the video image and obtain the video image sequence. The Roberts operator is used for edge detection and Gaussian smoothing of the video image. Secondly, the normalization method is used to extract the key features of moving targets in video images. Finally, training is carried out alternately to generate the countermeasure network model, and the video image moving target recognition sample results are output according to the training results to realize the video image moving target recognition. The experimental results show that the highest recognition accuracy of the proposed method is 98.1%, and the longest recognition time is only 5.7 s, indicating that its recognition effect is good.

Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network.

Meningioma is the second most commonly encountered tumor type in the brain. There are three grades of meningioma by the standards of the World Health Organization. Preoperative grade prediction of meningioma is extraordinarily important for clinical treatment planning and prognosis evaluation. In this paper, we present a new deep learning model for assisting automatic prediction of meningioma grades to reduce the recurrence of meningioma. Our model is based on an improved LeNet-5 model of convolutional neural network (CNN) and does not require the extraction of the diseased tissue, which can greatly enhance the efficiency. To address the issue of insufficient and unbalanced clinical data of meningioma images, we use an oversampling technique which allows us to considerably improve the accuracy of classification. Experiments on large clinical datasets show that our model can achieve quite high accuracy (i.e., as high as 83.33%) for the classification of meningioma images.