Regional Convolutional Neural Network for Cell Detection and Classification in Urinary Bladder Cancer

Abstract

Bladder canceristhefourth most common type of cancer in men and the eighth in women. Patient treated for this cancer must be monitored for the rest of their live due to the recurrence of this disease.That need for monitoring makes bladder cancer the most expensive cancer. Pathologists analyse histological images of tissues to evaluate grade of the cancer.This analyse is done manually, therefore it depends on depathologist and it is not reproducible. To fix that problem, in this thesis an automatic and reproducible method is proposed. The method proposed in this thesis use classical image processing and deep neural networks to compute the number and type of cells in a histological image. First, using classical image processing cells from the input images are automatically labelled, then that labels are manually corrected to create a ground truth. Using that ground truth a Faster Convolutional Neural Network is trained and a model is generated. That model is trained and tested in different scenarios and results are compared to decide which one is the best. Classical image processing shows a precision of 96.6% and recall of 64.6% detecting non-positive cells, while for positive those values are 82.4% and 66.7%, respectively. Using the best neuralnet work model the precision and recall detecting non-positive cells was 4.5% and 71.7%, while for positive cells was 0% and 0%.Those results means that classical image processing is still better than neural network, this latest method could be useful in the future, but it must be improved.