日本語フィールド
著者： 読み: Daigo Takano and Teruya Minamoto題名：A Detection Method for Early-Stage Colorectal Cancer Using Dual-Tree Complex Wavelet Packet Transform発表情報：Advances in Intelligent Systems and Computing book series 巻: 1421 ページ: 205-210キーワード：概要：Colorectal cancer is a major cause of death. As a result, cancer detection using supervised learning methods from endoscopic images is an active research area. Regarding early-stage colorectal cancer, preparing a significant number of labeled endoscopic images is impractical. We devise a technique for detecting early-stage colorectal cancer in this study. This technique consists of a 2D complex discrete wavelet packet transform and principal component analysis. As this technique does not require supervised learning, detection is feasible even in the absence of labeled data. In the endoscopic image, this technique correctly classifies early-stage colorectal cancer and normal regions with 92% accuracy. This approach outperforms the local binary pattern method.抄録：英語フィールド
Author：Title：A Detection Method for Early-Stage Colorectal Cancer Using Dual-Tree Complex Wavelet Packet TransformAnnouncement information:Advances in Intelligent Systems and Computing book series Vol: 1421 Page: 205-210An abstract：Colorectal cancer is a major cause of death. As a result, cancer detection using supervised learning methods from endoscopic images is an active research area. Regarding early-stage colorectal cancer, preparing a significant number of labeled endoscopic images is impractical. We devise a technique for detecting early-stage colorectal cancer in this study. This technique consists of a 2D complex discrete wavelet packet transform and principal component analysis. As this technique does not require supervised learning, detection is feasible even in the absence of labeled data. In the endoscopic image, this technique correctly classifies early-stage colorectal cancer and normal regions with 92% accuracy. This approach outperforms the local binary pattern method.