Affiliation:
1. Department of Computer Science and Engineering National Institute of Technology Hamirpur Hamirpur Himachal Pradesh India
2. Department of CSE Central University of Rajasthan Ajmer Rajasthan India
3. Department of Computer Science Engineering & IT Jaypee Institute of Information Technology Noida Uttar Pradesh India
Abstract
ABSTRACTClassifying fetal ultrasound images into different anatomical categories, such as the abdomen, brain, femur, thorax, and so forth can contribute to the early identification of potential anomalies or dangers during prenatal care. Ignoring major abnormalities that might lead to fetal death or permanent disability. This article proposes a novel hybrid capsule network architecture‐based method for identifying fetal ultrasound images. The proposed architecture increases the precision of fetal image categorization by combining the benefits of a capsule network with a convolutional neural network. The proposed hybrid model surpasses conventional convolutional network‐based techniques with an overall accuracy of 0.989 when tested on a publicly accessible dataset of prenatal ultrasound images. The results indicate that the proposed hybrid architecture is a promising approach for precisely and consistently classifying fetal ultrasound images, with potential uses in clinical settings.