ScholarWorks@동국대학교

초록

Computer-aided diagnosis (CAD) systems have been used to assist doctors (radiologists) in diagnosing many types of diseases, such as thyroid, brain, breast, and lung cancers. Previous studies have successfully built CAD systems using large, annotated datasets to train their models. The use of a large volume of training data helps these CAD systems to collect rich information for application in the diagnosis process. However, a large amount of training data is sometimes unavailable for training the models, such as for a new or less common disease and diseases that require expensive image acquisition devices. In such cases, conventional CAD systems are unable to learn their models efficiently. As a result, diagnostic performance is reduced. In this study, we focus on dealing with this problem; thus, our classification method can enhance the performance of conventional CAD systems based on the ensemble model of a support vector machine (SVM), multilayer perceptron (MLP), and few-shot (FS) learning network when working with small training datasets of brain tumor images. Through experiments, we confirmed that our proposed method outperforms conventional deep learning-based CAD systems when working with a small training dataset. In detail, we verified that the lack of training data led to the reduction of classification performance. In addition, we enhanced the classification accuracy from 3% to 10% compared to previous studies that used the SVM-based classification method or fine-tuning of a convolutional neural network (CNN) using two public datasets.

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