Classification of echocardiography images using Convolutional Neural Network to assist Kawasaki disease diagnosis

The Kawasaki disease is the most common heart condition affecting young children usually under five years old in developed countries and especially in Asia [1]. It damages blood vessels all over the body and results in vasculitis, myocarditis and coronary dilation causing long term heart complications and making it is essential to be able to detect the disease at an early state. One of the methods used to detect Kawasaki disease is to perform a 2D echocardiography to monitor the inflammation of heart muscles and the swelling of coronary arteries. The improvement of this technique is a cornerstone of a good treatment for these children. Based on the success of Convolutional Neural Networks to solve computer vision problems such as images classification, this master thesis aims to develop a system to ease the diagnosis of Kawasaki disease using echocardiographies focusing more specifically on coronary arteries. To do so, we will use deep learning classification techniques such as convolutional neural networks to extract the frames containing images of a coronary artery in a video of 2D echocardiography. These images can later be used to monitor the state of the coronary arteries.