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Breast cancer is the second leading cause of death in females. As such, women have high incidence and mortality rates of breast cancer. The incidence rate has been on the rise over time. The earlier breast cancer is caught, the better it shows prognosis and the lower the mortality rate is. For this reason, many researchers and medical doctors have heeded a lot of attention to the CAD systems to detect and classify breast cancer. They have proposed a myriad of methods and techniques. Among them, the CAD system based on artificial intelligence (AI) can process plenty of information fast, and its performance is evaluated to be high. As an AI algorithm, YOLO has excellent detection performance and can detect objects effectively in real time. In this paper, we proposed an anomaly detection model of mammography using a YOLOv4-based histogram. In terms of breast cancer diagnosis, mammography features a fast diagnosis time and an inexpensive cost. For this reason, it is often applied to breast cancer diagnosis. Mammography, however, generates an image only with brightness values, so that a mammogram image has a lot of noise and image edges are dim. To enhance these image edges, we create a difference through histogram and brightness range control and threshold-based region removal methods and expand the single channel of mammogram images using the generated images. Through the expansion, the image edges are enhanced and converted into a single channel again and are learned through YOLO. For performance evaluation, the method proposed in this study is compared with ResNet18, ResNet50, GoogleNet, and VGG16. According to an experiment, the proposed method had the highest accuracy, or 95.74%, followed by GoogleNet (89.9%), VGG16 (88.93%), ResNet50 (87.77%), and ResNet18 (87.67%) in order.

Multimedia provides a lot of information for users through various forms of information contents and information processing. These days, it is utilized and converged in diverse fields. Particularly, in the convergence of movie or TV drama media and information technologies, how to visualize or predict emotional changes on the basis of a variety of multimedia information has steadily been researched. Based on emotional changes, it is possible to analyze genres of movies or TV dramas. Viewers try to select a movie on the basis of their preferred storylines. In other words, users select and personalize the genres fitting their sentiment in the emotional flow of videos so that they want the customized media reflecting their emotional flow to be recommended. A typical method of predicting emotions shows the high accuracy of emotion prediction when text-based lines are utilized. Nevertheless, when subtle emotions in text lines are presented with voice and video information, it is possible to increase the accuracy of emotion prediction. Therefore, this study proposes a system that is capable of predicting emotional context primarily with the use of a text, and then with the analysis of emotions in the dialogs of characters or their voices in scenes, or images of characters at the point of time. In order to predict emotions efficiently, the proposed system analyzes the time information on a character’s particular emotion words from text data, extracts voice signals of the time section and converts them into a spectrogram, and saves the face image at the point of time. This imaged spectrogram and the face image for facial expression analysis are used as input data of CNN (convolutional neural network) and are trained. An emotion found in a particular paragraph is predicted, and therefore an emotional flow and an emotion in a particular scene are discerned. The developing multi-modal emotion prediction system collects the estimated emotions from convergence media and active contents (text, voice, and image) and active contents such as text, voice, and image. Finally, it predicts emotions.