Benchmarking the efficiency of deep learning methods on the problem of predicting subjects’ age by chest radiographs

Abstract

This paper presents results that were obtained in comparative study of the efficiency of conventional and Deep Learning methods on the problem of predicting subjects’ age by their chest radiographs. A large study group con- sisting of chest radiographs of 10 000 people was created by random sub-sampling of suitable subjects from the input image repository containing 1.8 million items. The age range was chosen to span from 21 to 70 years. The age prediction was performed by Convolutional Neural Networks AlexNet and GoogLeNet as well as using conventional methods based on Local Binary Patterns and extended co-occurrence matrices as image features followed by kNN, Random Forest, Linear Model, SVM, and Decision Trees classifiers. The conclusion was that the convolutional neural networks greatly outperform conventional methods. It was found that the lowest RMSE error achieved on the task of age prediction using convolutional networks is 5.77 years whereas conventional methods demonstrate on the same data much higher error value of 11.73 years.