Since the population of educated people gradually increases, studying a foreign language has become a common choice for a major or interest. Based on the general understanding of Second Language Acquisition, learners from varied regions tend to obtain different focuses on language learning. This paper focuses on linguistic correctness in the Chinese environment affecting the English learning process to specify the research topic. This paper uses qualitative analysis to differentiate and summarize the concept of linguistic correctness in China and its influence on English learning, implying that research can be a helpful resource for linguistics and education studies. In Asia, many countries have incorporated English into their teaching systems, and English has become one of the compulsory courses in education. Since China has a large population, English learners make up a considerable percentage of the total population. Thus, in this essay, the focus is to go deeper into their perceptions of the linguistic correctness of English, taking English learners in China as the main object of our study.

Purpose The aim of this study was to construct and validate a noninvasive radiomics method based on magnetic resonance imaging to differentiate sinonasal extranodal natural killer/T-cell lymphoma from diffuse large B-cell lymphoma. Methods We collected magnetic resonance imaging scans, including contrast-enhanced T1-weighted imaging and T2-weighted imaging, from 133 patients with non-Hodgkin lymphoma (103 sinonasal extranodal natural killer/T-cell lymphoma and 30 diffuse large B-cell lymphoma) and randomly split them into training and testing cohorts at a ratio of 7:3. Clinical characteristics and image performance were analyzed to build a logistic regression clinical-image model. The radiomics features were extracted on contrast-enhanced T1-weighted imaging and T2-weighted imaging images. Maximum relevance minimum redundancy, selectKbest, and the least absolute shrinkage and selection operator algorithms (LASSO) were applied for feature selection after balancing the training set. Five machine learning classifiers were used to construct the single and combined sequences radiomics models. Sensitivity, specificity, accuracy, precision, F1score, the area under receiver operating characteristic curve, and the area under precision-recall curve were compared between the 15 models and the clinical-image model. The diagnostic results of the best model were compared with those of 2 radiologists. Results The combined sequence model using support vector machine proves to be the best, incorporating 7 features and providing the highest values of specificity (0.903), accuracy (0.900), precision (0.727), F1score (0.800), and area under precision-recall curve (0.919) with relatively high sensitivity (0.889) in the testing set, along with a minimum Brier score. The diagnostic results differed significantly (P < 0.05) from those of radiology residents, but not significantly (P > 0.05) from those of experienced radiologists. Conclusions Magnetic resonance imaging based on machine learning and radiomics to identify the type of sinonasal non-Hodgkin lymphoma is effective and has the potential to help radiology residents for diagnosis and be a supplement for biopsy.