TPUnit neural network and simple ensemble for abnormal shadow detection in lung X-ray images

Abstract

We have constructed systems that detect abnormal areas of lung X-ray images from one-dimensional numeric sequences using neural networks. In these systems, the neural network consists of neurons that use trigonometric polynomials as activation functions, or TPUnit neural networks. The TPunit neural network has a high generalization ability in a smaller number of hidden units. Several TPUnit neural networks are placed in parallel and their outputs are processed as a simple ensemble. ROC curves denoted performance greater than that of previous reports. In addition, the AUC (area under curve) value was 0.9998 and the EER (equal error rate) was 0.5363%. Experimental results indicate that this proposed system is useful for medical imaging diagnosis.