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Tumor detection using medical images plays a key role in medical practices. One challenge in tumor detection is how to handle the nonlinear distribution of the real data. Owing to its ability of learning the nonlinear distribution of the tumor data without using any prior knowledge, one-class support vector machines (SVMs) have been applied in tumor detection. The conventional one-class SVMs, however, assume that each feature of a sample has the same importance degree for the classification result, which is not necessarily true in real applications. In addition, the parameters of one-class SVM and its kernel function also affect the classification result. In this study, immune algorithm (IA) was introduced in searching for the optimal feature weights and the parameters simultaneously. One-class immune feature weighted SVM (IFWSVM) was proposed to detect tumors in MR images. Theoretical analysis and experimental results showed that one-class IFWSVM has better performance than conventional one-class SVM.