Oral mucosal disease is likely to cause various disorders after treatment to occur in a domain called the oral cavity. Therefore, we are developing a diagnostic support system for early screening of oral mucosal disease. There is a problem of individual differences in the cut-out of the disease area from the original intraoral image in system development. In this study, we analyzed the relationships between cutout areas, extracted features and classification rates and investigated the relationship between individual differences. Therefore, we focused on how to eliminate the subjects. Group classification was then performed and identification was performed using an oral mucosal diagnosis support system with ensemble learning. The experimental results revealed relationships between the excision range, identification rate, and feature value.

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