  Echocardiography is a powerful prenatal examination tool for early diagnosis
of fetal congenital heart diseases (CHDs). The four-chamber (FC) view is a
crucial and easily accessible ultrasound (US) image among echocardiography
images. Automatic analysis of FC views contributes significantly to the early
diagnosis of CHDs. The first step to automatically analyze fetal FC views is
locating the fetal four crucial chambers of heart in a US image. However, it is
a greatly challenging task due to several key factors, such as numerous
speckles in US images, the fetal cardiac chambers with small size and unfixed
positions, and category indistinction caused by the similarity of cardiac
chambers. These factors hinder the process of capturing robust and
discriminative features, hence destroying fetal cardiac anatomical chambers
precise localization. Therefore, we first propose a multistage residual hybrid
attention module (MRHAM) to improve the feature learning. Then, we present an
improved YOLOv4 detection model, namely MRHAM-YOLOv4-Slim. Specially, the
residual identity mapping is replaced with the MRHAM in the backbone of
MRHAM-YOLOv4-Slim, accurately locating the four important chambers in fetal FC
views. Extensive experiments demonstrate that our proposed method outperforms
current state-of-the-art, including the precision of 0.919, the recall of
0.971, the F1 score of 0.944, the mAP of 0.953, and the frames per second (FPS)
of 43.
