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A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2‐weighted, T2‐mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2‐weighted (T2W) from T2‐weighted imaging, Apparent Diffusion Coefficient (ADC) from LSDI, and proton density (PD) and T2 (T2 Map) from T2‐mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor “ground truth.” Textural features were extracted from the images using co‐occurrence matrix (CM) and discrete cosine transform (DCT). Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack‐knife approach was used to evaluate our classifiers. Single‐channel maximum …