Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractModeling gas/particle partitioning of polybrominated diphenyl ethers (PBDEs) in the atmosphere: A review    Next AbstractEgg yolk immunoglobulin interactions with Porphyromonas gingivalis to impact periodontal inflammation and halitosis »

IEEE J Biomed Health Inform


Title:FLDS: An Intelligent Feature Learning Detection System for Visualizing Medical Images Supporting Fetal Four-Chamber Views
Author(s):Qiao S; Pang S; Luo G; Pan S; Chen T; Lv Z;
Address:
Journal Title:IEEE J Biomed Health Inform
Year:2022
Volume:20221004
Issue:10
Page Number:4814 - 4825
DOI: 10.1109/JBHI.2021.3091579
ISSN/ISBN:2168-2208 (Electronic) 2168-2194 (Linking)
Abstract:"Fetal congenital heart disease (CHD) is the most common type of fatal congenital malformation. Fetal four-chamber (FC) view is a significant and easily accessible ultrasound (US) image among fetal echocardiography images. Automatic detection of four fetal heart chambers considerably contributes to the early diagnosis of fetal CHD. Furthermore, robust and discriminative features are essential for detecting crucial visualizing medical images, especially fetal FC views. However, it is an incredibly challenging task due to several key factors, such as numerous speckles in US images, the fetal four chambers with small size and unfixed positions, and category confusion caused by the similarity of cardiac chambers. These factors hinder the process of capturing robust and discriminative features, hence destroying the fetal four chambers' precise detection. Therefore, we propose an intelligent feature learning detection system (FLDS) for FC views to detect the four chambers. A multistage residual hybrid attention module (MRHAM) presented in this paper is incorporated in the FLDS for learning powerful and robust features, helping FLDS accurately locate the four chambers in the fetal FC views. Extensive experiments demonstrate that our proposed FLDS outperforms the current state-of-the-art, including the precision of 0.919, the recall of 0.971, the F(1) score of 0.944, the mAP of 0.953, and the frames per second (FPS) of 43. In addition, our proposed FLDS is also validated on other visualizing nature images such as the PASCAL VOC dataset, achieving a higher mAP of 0.878 while input size is 608 x 608"
Keywords:"Echocardiography Female Fetal Heart/diagnostic imaging *Heart Defects, Congenital/diagnostic imaging Humans Pregnancy Ultrasonography, Prenatal/methods *Volatile Organic Compounds;"
Notes:"MedlineQiao, Sibo Pang, Shanchen Luo, Gang Pan, Silin Chen, Taotao Lv, Zhihan eng Research Support, Non-U.S. Gov't 2021/06/23 IEEE J Biomed Health Inform. 2022 Oct; 26(10):4814-4825. doi: 10.1109/JBHI.2021.3091579. Epub 2022 Oct 4"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 21-11-2024