Deep Learning Approach for Classifying Papanicolau Cervical Smears
José Martínez-Más, Andrés Bueno-Crespo, Baldomero Imbernón, José Maria Cecilia, Raquel Martínez‐España, Manuel Remezal‐Solano, Alberto Sánchez-Espinosa, Sebastián Ortiz-Reina, Juan-Pedro Martínez-Cendán · 2018
Cervical cancer is the third neoplasm in frequency worldwide between women. Screening techniques in general population have demonstrated clear effectiveness as its implementation has decreased cervical cancer incidence and mortality more than 70% in several countries. This benefit is related with detection of early pre-malignant asymptomatic lesions, that can be treated to avoid their progression to invasive cancer. Papanicolau cervical smear is the most common cancer screening technique worldwide used since described by Giorgios Papanicolau on 1928. Sampling techniques have been improved in last years, based on simplifying and automatizing procedures. However, after preparing the samples, an expert review of the microscopic images is needed. There are few automatic diagnostic methods published, but their results are not as good as an expert examination. In this paper, we develop a microscopic cervical cells database using Papanicolau cervical smears from our patients, sampled few minutes before performing a cone biopsy on them. With this procedure, we have both the cervical smear and the biopsy diagnostics, tagged as Gold Standard. Then, a deep-learning methodology is performed for the automatic categorization of pre-malignant and benign cervical cells. We use the the Caffe deep-learning framework to leverage NVIDIA GPU computing architectures to deal with this real patient database in a reduced time-frame. Our results reveal the deep learning methodology is robust in this biomedical classification, reaching up to 78% Accuracy.