Abstract
Classification is one well-known type of supervised machine learning and has been developed and applied in the medical, and industrial fields. Currently, single machine learning models, such as support vector machine (SVM) and decision tree are widely implemented, but the difficulty of embedding human knowledge in application with image datasets impedes application extension. To address this problem, we propose a category classification expert system (CCES), which consists of a single semantic segmentation network model, deep learning network model, and classification machine learning model. Unlike a single machine learning model with limited functionality, CCES can embed human expert knowledge into the system, increasing model accuracy, sensitivity, and adaptability of the input with image datasets in the category classification field. We describe a medical category classification application using CCES in detail and describe the evaluation results.
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