Automatic classification of light field smear microscopy patches using Convolutional Neural Networks for identifying mycobacterium tuberculosis

Tuberculosis (TB) has been included among the top ten leading causes of death worldwide. Since 2008, several investigations have been developed by scientific community for automatic detection of mycobacterium tuberculosis (MT) in light field microscopy images. Those authors have applied techniques as digital images processing and machine learning, combined with different sizes of datasets and color spaces such as: RGB, HSI and Lab. More recently, deep learning has been used, working with grayscale, though. We present a method for automatic classification of light field smear microscopy patches using RGB, R-G and grayscale patches versions as inputs of a Convolutional Neural Networks (CNN) model for identifying MT. A dataset of negative and positive patches was created for training three CNN Models applying regularization techniques, like: dataset normalization, data augmentation and dropout. The best result in the patch classification test was reached using R-G input version and a three convolutional layers Model with regularization, getting an Area under ROC Curve of 99%.