$GTCH's goal of the research is to enable a machine learning algorithm to decide as to whether an image’s object is of interest or not, pointing a possible health related conclusion. A Kirlian image contains a huge amount of data. The detection and analytics of a Kirlian image requires major computational capabilities in a real time operation. A neural network analysis is done to ensure a reliable object’s classification and to train for the detection of objects of interest within a Kirlian image. The approach performs an image based color-based pre-processing, to reach a conclusion about certain pattern and color presented in the image. The goal is to reach a real-time Kirilian image processing with the use of deep learning algorithms and supporting computational hardware resources, achieving advanced imaging conclusions that may provide health related information.