Complexity Evaluation of Medical Image Data for Classification Problem Based on Spectral Clustering
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama · 2020
Deep convolutional neural networks (DCNNs) have been popular with medical image classification problems in recent years. However, training a DCNN model on the sizeable medical dataset requires repeated manipulation to achieve the desired results and hence is time-consuming. Since there is an inevitable link between DCNN training results and the complexity of the medical dataset, it is essential to accurately evaluate the medical dataset's complexity before training the DCNN models. In this paper, we propose an efficient method to assess the medical dataset's complexity based on spectral clustering. The experimental results show that the medical dataset complexity calculated with our approach is not time-consuming and has a high correlation with DCNN test accuracy.