  The Epithelial Dysplasia (ED) is a tissue alteration commonly present in
lesions preceding oral cancer, being its presence one of the most important
factors in the progression toward carcinoma. This study proposes a method to
design a low computational cost classification system to support the detection
of dysplastic epithelia, contributing to reduce the variability of pathologist
assessments. We employ a multilayer artificial neural network (MLP-ANN) and
defining the regions of the epithelium to be assessed based on the knowledge of
the pathologist. The performance of the proposed solution was statistically
evaluated. The implemented MLP-ANN presented an average accuracy of 87%, with a
variability much inferior to that obtained from three trained evaluators.
Moreover, the proposed solution led to results which are very close to those
obtained using a convolutional neural network (CNN) implemented by transfer
learning, with 100 times less computational complexity. In conclusion, our
results show that a simple neural network structure can lead to a performance
equivalent to that of much more complex structures, which are routinely used in
the literature.
