Non-specialist Versus Neural Network

The general paradigm is the following: more input information for a system leads to a better performance. Our assumption is that the information must be rather appropriate than excessive, especially, when the training data is limited. Moreover, the requirements of a machine learning system might differ from the ones of a human observer. In this work, we analyze and compare the performance of several neural network architectures and human readers, who had only basic common knowledge on the subject. The example task is gender classification using abdominal computerized tomography (CT) data. It has been demonstrated by our study that training of a neural network purely on pelvic bone segmentation masks is the most efficient and produces highly accurate results, whereas for human observers this information is not sufficient. The study confirms our original assumptions and emphasizes the importance of the appropriate input feature selection for a better performance of a specific task.