Using Machine Learning to Diagnose Chest X-rays and Interpret Patient Symptoms and Medical History

Creating a large scale, automated method by which multiple convolutional recurrent neural networks are applied to classify anteroposterior, posteroanterior, and lateral thoracic radiographs; these findings are simultaneously clinically correlated with patient prodromes and medical history to output a consolidated diagnosis. Collaborating with students from Daegu Science High School in Daegu, South Korea.