Technologies: Deep Learning, CNN, SVM, statistics We’ve done a meta-analysis of 600+ scientific papers studying automated methods for Lung Cancer detection in X-ray images for the period from 1993 till 2020. The goal was to determine the effectiveness of Deep Learning-based methods including Convolutional Neural Networks (CNN) compared to classical methods such as support vector machines (SVM), decision trees, random forests, linear discriminant analysis (LDA), clustering methods etc. Meta-analysis includes comparing the two groups of methods using statistical metrics, forest plot and funnel plot.

Technologies: Python, OpenCV Duration: 6 months The goal of the project is to analyze the video of a tennis game for breaking match into shorter videos: one video per point. It was required to remove those parts of the match where the players did not play (the players rest, the gap between the points, etc.); that allowed game statisticians to make further revisions of the game much faster because all "idle" periods of the game were removed and the total length (as soon as file size) was much shorter. The logic of breaking video has been developed based on the analysis of game events that were detected in the video, position, speed and posture of players, ball movement and location, and other parameters. CV algorithms were used: optic flow, background subtraction, HoG detector, pose detection and others.