Cardiovascular disease (CVD) is the most common type of disease and has a high fatality rate in humans. Early diagnosis is critical for the prognosis of CVD. Before using myocardial tissue strain, strain rate, and other indicators to evaluate and analyze cardiac function, accurate segmentation of the left ventricle (LV) endocardium is vital for ensuring the accuracy of subsequent diagnosis. For accurate segmentation of the LV endocardium, this paper proposes the extraction of the LV region features based on the YOLOv3 model to locate the positions of the apex and bottom of the LV, as well as that of the LV region; thereafter, the subimages of the LV can be obtained, and based on the Markov random field (MRF) model, preliminary identification and binarization of the myocardium of the LV subimages can be realized. Finally, under the constraints of the three aforementioned positions of the LV, precise segmentation and extraction of the LV endocardium can be achieved using nonlinear least-squares curve fitting and edge approximation. The experiments show that the proposed segmentation evaluation indices of the method, including computation speed (fps), Dice, mean absolute distance (MAD), and Hausdorff distance (HD), can reach 2.1–2.25 fps, , mm, and mm, respectively. This indicates that the suggested method has better segmentation accuracy and robustness than existing techniques.

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi‐features and support vector machines was proposed for breast tumor diagnosis. Multi‐features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi‐features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the onlineshopping10cats dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively.

Data-driven machine learning is increasingly involved in human life and industrial development due to its large-scale testing and low time cost. However, existing learning algorithms are not suitable for real-world applications with data dilemmas, such as extremely high-dimension-low-sample-size problems, non-Gaussian noise, and uncertainty. In this article, we propose a novel fuzzy multikernel subspace learning (FMKSL) to address these problems, which provides a robust multikernel representation with a fuzzy constraint and sparse coding. We then develop an adaptive learner chain optimization method based on the iterative process of FMKSL to speed up learning and achieve the best performance. Different from previous methods, we also design a flexible data augmentation method, namely generalized correntropy-based adaptive data augmentation (GC-ADA), to effectively use the $\alpha$ -order statistics between samples to transform the exact value prediction task into a simpler classification one. It is important that our general framework only needs an extremely small dataset to predict the related ranking of the sample since the exact label value measured by different institutions in reality varies largely. A typical scenario is the drug screening task, i.e., the inhibitory potency prediction of the nicotinamide phosphoribosyltransferase inhibitors. Extensive experiments on nine real-world datasets (four tasks) show that our framework outperforms state-of-the-art methods in prioritizing candidate samples and chemicals for experimental research and analysis via a data-driven computational approach.