Enhanced Fetal Brain Ultrasound Image Diagnosis Using Deep Convolutional Neural Networks

EasyChair Preprint 15347

Abstract

This article explores the use of deep convolutional neural networks (CNNs) to enhance the diagnosis of fetal brain abnormalities through ultrasound imaging. Fetal brain development is critical for neurological health, and early detection of anomalies can significantly impact patient outcomes. Traditional diagnostic methods rely on manual interpretation, which can be subjective and time-consuming. This study leverages a comprehensive dataset of fetal brain ultrasound images, employing advanced CNN architectures to automate image analysis and improve diagnostic accuracy. The methodology includes data preprocessing, model training, and evaluation against established performance metrics such as accuracy, sensitivity, and specificity. Results indicate that the CNN model outperforms traditional diagnostic approaches, providing reliable identification of various fetal brain abnormalities. The findings highlight the potential for integrating CNNs into clinical practice, offering faster and more accurate diagnoses while addressing current limitations in ultrasound interpretation. Future research directions emphasize expanding datasets and enhancing model robustness to further improve prenatal care.

Keyphrases: Traditional diagnostic, clinical practice, ensitivity and specificity

@booklet{EasyChair:15347,
  author    = {George Christopher and Peter John and Bolu Tokode},
  title     = {Enhanced Fetal Brain Ultrasound Image Diagnosis Using Deep Convolutional Neural Networks},
  howpublished = {EasyChair Preprint 15347},
  year      = {EasyChair, 2024}}