This paper mainly discusses fractional differential approach to detecting textural features of digital image and its fractional differential filter. Firstly, both the geometric meaning and the kinetic physical meaning of fractional differential are clearly explained in view of information theory and kinetics, respectively. Secondly, it puts forward and discusses the definitions and theories of fractional stationary point, fractional equilibrium coefficient, fractional stable coefficient, and fractional grayscale co-occurrence matrix. At the same time, it particularly discusses fractional grayscale co-occurrence matrix approach to detecting textural features of digital image. Thirdly, it discusses in detail the structures and parameters of n×n any order fractional differential mask on negative x-coordinate, positive x-coordinate, negative y-coordinate, positive y-coordinate, left downward diagonal, left upward diagonal, right downward diagonal, and right upward diagonal, respectively. Furthermore, it discusses the numerical implementation algorithms of fractional differential mask for digital image. Lastly, based on the above-mentioned discussion, it puts forward and discusses the theory and implementation of fractional differential filter for digital image. Experiments show that the fractional differential-based image operator has excellent feedback for enhancing the textural details of rich-grained digital images. fractional stationary point, fractional equilibrium coefficient, fractional stable coefficient, fractional grayscale co-occurrence matrix, fractional differential mask
Background: Ideal tools should not only investigate risk factors, but also provide explicit auxiliary answer for whether a patient will develop surgical site infection (SSI) or not. Machine learning (ML) models have ability to carry out complicated predictive medical tasks. We intend to develop ML models to predict SSI after posterior cervical surgery and interpret the outcome. Methods: We retrospectively analyzed 235 patients who had undergone posterior cervical surgery between June 2013 to April 2019 at Zhongda Hospital Affiliated to Southeast University. We established Artificial neural networks (ANN), XGBClassifier (xgboost), KNeighborsClassifier (KNN), Decision tree classifier (decision tree), Random forest classifier (random forest) and support vector classifier (SVC). Receiver operating characteristic (ROC) curve, area under the curve (AUC) score, accuracy score, recall score, F1 score and precision score were calculated to measure models’ performance. Shapley values were calculated using SHapley Additive exPlanations (SHAP) to determine relative feature importance of xgboost model. Results: The incidence of SSI was 7.23%. With AUC of 0.9972, 0.9923, 0.9865, 0.9615, 0.9540, 0.8934, the xgboost, random forest, ANN, KNN, decision tree, SCV accurately predicted SSI. Xgboost, ANN, decision tree and random forest achieved excellent performance in testing set. Top 10 variables with high predictive contribution of xgboost including, drainage volume, body mass index (BMI), drainage duration, operation blooding, cholesterin, sex, prognostic nutritional index (PNI), albumin, hypertension, operation time. Conclusion: We had successful established ML models in individualized predicting SSI after posterior cervical surgery. Xgboost, ANN, decision tree and random forest achieved excellent performance which could provide auxiliary information for clinical decision makers. The interpretable model focuses on contribution of important features to the predictive result. It can improve the acceptance of clinicians on ML and promote ML’s application in the actual clinical work.
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