Tesis de Maestría / master Thesis
A deep-learning application for epithelial cells image detection
Fecha
2021-09-16Registro en:
Autor
CORTES CAPETILLO, AZAEL JESUS; 366841
Anaya Alvarez, Sergio Eduardo
Institución
Resumen
Urinary particles are used to evaluate the different urinary tract diseases in patients.
Currently, doctors use the traditional methods for urinalysis such as urine dipstick, urine
culture and microscopy. Microscopy is an effective method for the diagnosis and treatment
of many kidney and urinary tract diseases. However, manual microscopic examination of
urine is labor-intensive, subjective, imprecise, and time-consuming. In this project, we
proposed the development of a different deep learning models classifier for an automated
microscopic urinalysis system for epithelial cells. A dataset was constructed from scratch
taking urine samples from the Hospital Ginequito obtaining a total of 857 images. Then, the
images were labeled into urine samples with and without epithelial cells for binary
classification. Last, we created three deep learning models using the InceptionV3
architectures with different series of fully connected layers randomly initialized and ReLU
activation, a dropout rate of 0.2 and a final sigmoid layer for classification. The best model
obtained a training accuracy of 81.89% with sensitivity of 77.84%, specificity of 85.94% and
precision of 84.70% and a validation accuracy of 84.28% with a sensitivity of 87.50%,
specificity of 81.25% and precision of 82.35%. It was concluded that microscopic urinalysis
can be done automatically, this opens the door for the classification of more urine particles
with improved metrics.