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.

Технологии: Python, OpenCV Продолжительность: 6 месяцев 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. Логика разбивки видео разработана на основе анализа игровых событий, которые были обнаружены на видео; положения, скорости и позы игроков, движения и местоположения мяча и других параметров. Использовались CV-алгоритмы: оптический поток, вычитание фона, HoG-детектор, определение позы и другие.