Since microaneurysms (MAs) can be seen as the earliest lesions in diabetic retinopathy, its detection plays a critical role in the diabetic retinopathy diagnosis. In recent years, many machine-learning methods have been developed for MA detection. Generally, MA candidates are first identified and then a set of features for these candidates are extracted. Finally, machine-learning methods are applied for candidate classification. In this paper, we present a novel unsupervised classification method based on sparse posterior cerebral artery (PCA) for MA detection. Since it does not have to consider a non-MA training set, the class imbalance problem can be avoided. Furthermore, effective features can be selected due to the characteristic of sparse PCA, which combines the elastic net penalty with the PCA. Meanwhile, a single T ² statistic is introduced, and the control limit can be determined for distinguishing true MAs from spurious candidates automatically. Experiment results on the retinopathy online challenge competition database show the effectiveness of our proposed method.

Exudates can be regarded as one of the most prevalent clinical signs of diabetic retinopathy, and the detection of exudates has important clinical significance in diabetic retinopathy diagnosis. In this paper, a novel approach named superpixel multi-feature classification for the automatic detection of exudates is developed. First, an entire image is segmented into a series of superpixels considered as candidates. Then, a total of 20 features, including 19 multi-channel intensity features and a novel contextual feature, are proposed for characterizing each candidate. A supervised multi-variable classification algorithm is also introduced to distinguish the true exudates from the spurious candidates. Finally, a novel optic disc detection technique is designed to further improve the performance of classification accuracy. Extensive experiments are carried out on two publicly available online databases, DiaretDB1, and e-ophtha EX. Compared with other state-of-the-art approaches, the experimental results show the advantages and effectiveness of the proposed approach.

Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.