Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. In this paper, we proposed a deep learning method based on Convolutional Neural Network to predict the stock price movement of Chinese stock market. We set the opening price, high price, low price, closing price and volume of stock deriving from the internet as input of the architecture and then run and test the program. The result has shown that it is a bit reliable to use deep learning method based on Convolutional Neural Network to predict the stockprice movement of China.

The lesion regions of a medical image account for only a small part of the image, and a critical imbalance exists in the distribution of the positive and negative samples, which affects the segmentation performance of the lesion regions. Dice loss is beneficial for the image segmentation involving an extreme imbalance of the positive and negative samples but it ignores the background regions, which also contain a large amount of information. In this work, we propose an improved dice loss that can mine the information in background areas and modify network architecture to improve performance. The improved dice loss called weighted soft dice loss (WSDice loss). Our loss function gives a small weight to the background area of the label, so the background area will be added to the calculation when calculating dice loss. It can also soft the hard label in the lesion area to increase the robustness of the model to noise label. What’s more, we propose to cascade Focal loss and WSDice loss. Focal Loss is a Distribution-based loss function, WSDice Loss is a Regionbased loss function, the optimization directions of them are different. The cascaded loss function can make full use of the advantages of both and greatly improve model performance. In addition, we add a simple but effective channel attention module to the decode module of U-net. We experimented on the ChestX-ray8 datasets. Compared with Dice loss, WSDice loss improves the dice coefficient by 1.59%, cascaded loss function can improve dice coefficient by 7.81%. The improved in model architecture can increase the dice coefficient by 1.36%.

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of “virtual dual-energy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated “extremely subtle” or “very subtle” by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially.

Over the past few years, researchers have demonstrated the possibilities to use the Computer-Aided Diagnosis (CAD) to provide a preliminary diagnosis. Recently, it is also becoming increasingly common for doctors and computer practitioners to collaborate on developing CAD. Since the early diagnosis of breast cancer is the most critical step, a precise segmentation of breast tumor with accurate edge and shape is vital for accurate diagnoses and reduction in the patients’ pain. In view of the deficient accuracy of existing method, we proposed a novel method based on U-Net to improve the tumor segmentation accuracy in breast ultrasound images. First, Res Path was introduced into the U-Net to reduce the difference between the feature maps of the encoder and decoder. Then, a new connection, dense block from the input of the feature maps in the encoding-to-decoding section, was added to reduce the feature information loss and alleviate the vanishing gradient problem. A breast ultrasound database, which contains 538 tumor images, from Xinhua Hospital in Shanghai and marked by two professional doctors was used to train and test models. We, using ten-fold cross-validation method, compared the U-Net, U-Net with Res Path, and the proposed method to verify the improvements. The results demonstrated an overall improvement by the proposed approach when compared with the other in terms of true-positive rate, false-positive rate, Hausdorff distance indices, Jaccard similarity, and Dice coefficients.