A DWT-Utilized Classifier for UPJO Diagnosis Using Ultrasound Images - 文章详情页

作者：

Guan, Yu (Guan, Yu.) | Wen, Pengceng (Wen, Pengceng.) | Li, Jianqiang (Li, Jianqiang.) | Ma, Zhilong (Ma, Zhilong.)

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摘要：

Ureteropelvic　Junction　Obstruction　(UPJO)　is　a　common　hydronephrosis　disease　in　children　that　can　result　in　even　progressive　loss　of　renal　function.　Ultrasonography　as　a　preliminary　diagnostic　step　for　UPJO　has　the　nature　of　economical,　radiationless,　noninvasive,　and　high-noise.　Artificial　intelligence　has　been　widely　applied　to　medical　fields　and　can　greatly　assistant　for　doctors＇　diagnostic　ability.　We　build　and　test　a　DWT-utilized　classifier　for　UPJO　diagnosis　using　ultrasound　images.　Our　diagnosis　model　is　a　combination　of　an　attention-based　pyramid　semantic　segmentation　network　and　a　discrete　wavelet　transformation　processed　residual　classification　network.　We　also　compare　the　performance　between　benchmark　models　and　our　models.　Our　diagnosis　model　outperformed　benchmarks　on　classification　task　with　accuracy=91.77%.　This　model　can　automatically　grade　the　severity　of　UPJO　by　ultrasound　images,　assistant　for　doctors＇　diagnostic　ability,　and　relieve　patients＇　burden.　©　2022　IEEE.

关键词：

Image classification Computer vision Machine learning Medical imaging Semantics Medical information systems Learning systems Medical computing Signal reconstruction Data mining Semantic Segmentation Data handling Classification (of information) Benchmarking Discrete wavelet transforms Computer aided diagnosis Ultrasonics

作者机构：

[ 1 ] [Guan, Yu]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 2 ] [Wen, Pengceng]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 3 ] [Li, Jianqiang]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 4 ] [Ma, Zhilong]School of Information Management, Xinjiang University of Finance and Economy, Xinjiang, China

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