Prostate Cancer Classification using Multispectral Imagery and Meta Heuristics
The introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. Unlike conventional RGB color space, it allows the acquisition of large number of spectral bands within the visible spectrum. The major problem arising in using multispectral data is high-dimensional feature vector size. The number of training samples used to design the classifier is small relative to the number of features. For such a high dimensionality problem, pattern recognition techniques suffer from the well-known curse-of-dimensionality problem. The aim of this book chapter is to discuss and compare various computational intelligence algorithms proposed recently by authors for the detection and classification of prostatic tissues using multispectral imagery.