The ultrasound examination is a difficult operation because a doctor not only operates an ultrasound scanner but also interprets images in rea time, which may increase the risk of overlooking tumors. To prevent that, we study a liver tumor detection method using convolutional neural networks toward realizing computer-assisted diagnosis systems. In this paper, we propose a liver tumor detection method within a false positive reduction framework. The proposed method uses YOLOv3  in order to find tumor candidate regions in real-time, and also uses VGG16  to reduce false positives. The proposed method using YOLOv3  and VGG16  achieved an F-measure of 0.837, which showed the effectiveness of the proposed method for liver tumor detection. Future work includes the collection of training data from more hospitals and their effective use for improving the detection accuracy.