Abstract
The oral exfoliative cytology allows a quick and fairly accurate assessment of suspicious lesions of the oral cavity. Within this context, our paper proposes a quantitative approach, focusing on the construction of a classifier for detecting the presence of the tumoral cells on oral smears. The design of the classifier relies on a detailed computerized analysis of the individual morphometric features exhibited by two large known populations of normal and tumoral cells, respectively; the digital image processing was performed in the Zeiss KS400 environment. The classifier was implemented as a neural network with step activation function, whose parameters were obtained from an adequate training, based on the nuclear and cytoplasmic areas of the cells belonging to the two populations. Our procedure based on this classifier was meant to operate by identifying the tumoral or normal nature of any cell randomly selected from a smear. To identify the nature of an arbitrary cell, its nuclear and cytoplasmic areas are presented at the input of the classifier. The classification procedure was tested on several smears, and the results coincided with the pathological diagnosis in all the considered cases. The performances of our approach are discussed in comparison with other analytical methods previously reported in oral exfoliative cytology. These discussions emphasize the role of numerical information exploited for the classifier design, concluding that the individual morphometric features are more meaningful than the global characterization of smears by mean values.