Improving Classification Performance by Pre-training Heterogeneous Data

Abstract

As a representative method of pre-learning in machine learning, it is known that using a prelearning model using ImageNet is useful for improving performance classification. In this research, we used data recorded by different shooting methods for different model and verified how the performance classification of machine learning was improved by incorporating the data into prelearning. Data from TMS, a corneal tomography measurement device and CASIA, an anterior segment OCT measurement device, both of which are marketed by Tomey, were used. These two Device are capable of photographing a map of the refractive index of the cornea, although their imaging methods are different. We found that pre-training using data of different shooting methods taken by different devices improved the classification performance more than machine learning models without pre-training. Even if the imaging method is different, if the data obtained by photographing the same parts(Cornea) and obtaining similar output is used for pre-learning, it can be said that the classification performance of the machine learning model will be improved.