Dissolved oxygen is an important index to evaluate water quality, and its concentration is of great significance in industrial production, environmental monitoring, aquaculture, food production, and other fields. As its change is a continuous dynamic process, the dissolved oxygen concentration needs to be accurately measured in real time. In this paper, the principles, main applications, advantages, and disadvantages of iodometric titration, electrochemical detection, and optical detection, which are commonly used dissolved oxygen detection methods, are systematically analyzed and summarized. The detection mechanisms and materials of electrochemical and optical detection methods are examined and reviewed. Because external environmental factors readily cause interferences in dissolved oxygen detection, the traditional detection methods cannot adequately meet the accuracy, real-time, stability, and other measurement requirements; thus, it is urgent to use intelligent methods to make up for these deficiencies. This paper studies the application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors. The combined application of optical detection technology, new fluorescence-sensitive materials, and intelligent technology is the focus of future research on dissolved oxygen sensors.

Breast cancer is a high-incidence type of cancer for women. Early diagnosis plays a crucial role in the successful treatment of the disease and the effective reduction of deaths. In this paper, deep learning technology combined with ultrasound imaging diagnosis was used to identify and determine whether the tumors were benign or malignant. First, the tumor regions were segmented from the breast ultrasound (BUS) images using the supervised block-based region segmentation algorithm. Then, a VGG-19 network pretrained on the ImageNet dataset was applied to the segmented BUS images to predict whether the breast tumor was benign or malignant. The benchmark data for bio-validation were obtained from 141 patients with 199 breast tumors, including 69 cases of malignancy and 130 cases of benign tumors. The experiment showed that the accuracy of the supervised block-based region segmentation algorithm was almost the same as that of manual segmentation; therefore, it can replace manual work. The diagnostic effect of the combination feature model established based on the depth feature of the B-mode ultrasonic imaging and strain elastography was better than that of the model established based on these two images alone. The correct recognition rate was 92.95%, and the AUC was 0.98 for the combination feature model.