Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.

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Nowadays, parking problem in urban cities has getting more and more urgent. Most of the problems are mainly due to the information share and management. In the work, we develop a cloud platform based on Huawei soft Ker cloud. On the cloud platform, we build a new smart parking platform, and implement methods, programs, and simulation test system for design and debugging. The functions of license plate recognition, car search and navigation, payment, parking lot search, intelligent parking are implemented in the platform through code debugging.

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