Neural net‐based identification of cells expressing the p300 tumor‐related antigen using fluorescence image analysis

We report on preliminary investigations of the use of an image analysis system to perform preliminary algorithmic classification of images of fluorochrome‐labeled cells followed by capture of gray‐level images of potentially abnormal cells for analysis by a neural network. Cells were labeled with an antibody against a bladder cancer tumor‐associated antigen, and the neural net was used to distinguish true‐positive cells from negative cells, false‐positive cells (autofluorescent or nonspecific labeling), and cell‐sized artifacts. Gray‐level cell images were digitized and processed for analysis by a feed‐forward neural network using back‐propagation. The network was trained and tested with two independent image sets. Various network configurations and activation functions were investigated, including a sinusoidal activation function. At high power, the network agreed completely with the human observer's classification. At low power, a strong clustering of cells classified by the network with expert classification was seen, while the neural network showed roughly 75% concordance with the human observer. In addition, a set of four features extracted from raw cell images were investigated. The features were: shape factor, texture, area, and average pixel intensity. A network trained with these features performed better than one operating with gray‐level images. We conclude that using neural networks to recognize and classify images captured by an image analysis microscope is feasible. Cytometry 27:36–42, 1997. © 1997 Wiley‐Liss, Inc.