Fuzzy Attention U-Net Architecture Based Localization and YOLOv5 Detection for Fetal Cardiac Ultrasound Images

Abstract

The importance of early identification of congenital heart disease is highlighted by the fact that it accounts for around 28% of all congenital abnormalities this is the primary reason why fetus die. The necessity of having a thorough understanding of normal cardiac architecture has been highlighted by the quick advancements in fetal heart imaging techniques that have occurred in recent years. Without this information, it is challenging, even impossible, to distinguish the many manifestations of congenital heart illness. This research suggests an immediate fetal cardiac identification technique employing US pictures with the You Only Look Once v5 (YOLOv5) framework and localization using Fuzzy Attention U-Net (FAU-Net) framework in order to enhance the interpretation of the anatomy of the fetal heart through Ultrasound (US) for precise and instantaneous diagnoses. Localization is accomplished using a FU-Net architecture, which limits the training set to image-level plane labels. This is a crucial component of the proposed study since, for big datasets, it would take too much time to produce bounding box annotations, which are not always captured. The FAU-Net design has been optimized for best performance. The YOLOv5 framework is built to function in real-time and deliver the best results for object identification. With the aid of appropriate fine-tuning, it may function effectively to automatically identify tiny fetal cardiac objects in a fast phase. Depending on how much of a detection overlapped with ground-truth bounding boxes, it was determined if it was a genuine positive or a false positive. This research primarily aids medical professionals in the diagnosis of fetal cardiac anatomy. The efficiency of the outcomes is evaluated using metrics including accuracy, memory, average precision (AP), mean average precision (mAP), and F-measure.