E. Machine Learning Classifier

After creating the optimal image feature vector of 12 radiomics features, we built a multi-feature fusion-based machine learning model or classifier aiming to distinguish or classify between the malignant and benign pancreatic tumors depicting on CT images. Although many different machine learning classifiers have been investigated and used in developing CAD schemes of medical images, we selected to build a widely used support vector machine (SVM) classifier due to its advantages working with the small image datasets and higher generalizability. SVM is also an efficient hyperplane defining and learning algorithm, which can be easily trained and implemented. Specifically, we selected to use a multi-layer perceptron (MLP) kernel that is generated from the neural organized hypothesis to build the SVM classifier. Because the SVM model using sigmoid kernel function is equivalent to two-layer perceptron neural network, we analyzed that MLP not only maps an eigenvector from the original d-dimensional space, but also maps an eigenvector from an intermediate implicit Hilbert feature space in which the inner product is computed. The learning kernel replaces the regular inward item between the weight vector and the input vector. In this way, the generalization ability of the general function approximator can be efficiently improved. Another reason is that MLP kernel have higher accuracy compared to other kernels.

In order to avoid bias created in case participation between the training and testing sub-datasets from a limited small dataset, we applied a standardized leave-one-case-out (LOCO) cross-validation method to train and test this SVM model to classify between the malignant and benign pancreatic tumors. Using the LOCO method, radiomics feature vectors computed from 76 tumors are used to train SVM model and the radiomics feature vector of one remaining tumor that is not involved in training process is tested by the trained model. Such LOCO process is iteratively performed 77 times. Thus, each of 77 pancreatic tumors are independently tested once by the SVM models trained using other 76 pancreatic tumors.