Open Access Open Access  Restricted Access Subscription Access

Early Detection of Fetal Brain Abnormalities Using YOLO V5 Model

Prof. Mary stella, Mohammad Yusuf, Mohammed Faizan Ali, Mohammed Thanzeem, Mohammed Sadath

Abstract


The detection of fetal brain abnormalities holds paramount importance in prenatal healthcare, as it directly influences medical decisions, treatment strategies, and patient outcomes. Fetal brain abnormalities refer to structural or func- tional deviations from the normal development of the fetal brain, and they can manifest in various forms, including malformations, anomalies, or disorders. We focus on improving the accuracy of fetal brain abnormality detection using advanced machine learning techniques. Specifically, we employ the YOLO V5 model, a state-of-the-art object detection algorithm, to detect abnormalities in fetal brain images. By leveraging the capabilities of deep learning and computer vision, we aim to enhance the efficiency and reliability of anomaly detection, thereby enabling early intervention and improved patient outcomes. Through this approach, we seek to contribute to the advancement of prenatal healthcare by providing clinicians with a powerful tool for the early identification and management of fetal brain abnormalities.


Full Text:

PDF

References


MDatta Singh Goolaub; iawei Xu; Eric M. Schrauben “Volumetric Fetal Flow Imaging With Magnetic Resonance Imaging”, (2022).

Lucilio Cordero-Grande; Juan Enrique Ortun˜o-Fisac, ”Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Reg- istration” (2022).

Karthikeyan kalyanasundaram balasubramanian, Francesco diotalevi, Claudio lorini, Andrea cavallo, Novella pretti and Dario paladini, “A Transcutaneous Fetal Visual Stimulator” ,(2022).

Vishal Singh; Pradeeba Sridar; Jinman Kim, “Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images Using Convolutional Neural Networks” (2021).

Aengus Daly, Alison O’Shea, Gordon Lightbody and Andriy Temko “Towards Deeper Neural Networks for Neonatal Seizure Detection” , (2021).

Chiara Di Vece, Student Member, Maela Le Lous, Brian Dromey, Francisco Vasconcelos And Anna L David,, “Ultrasound plane pose regression: assessing generalized pose coordinates in the fetal brain”

, (2021).

Ruowei Qu; Guizhi Xu; Chunxia Ding, “Standard Plane Identification in Fetal Brain Ultrasound Scans Using a Differential Convolutional Neural Network”,(Feb 2020).

Yibo Han; Zheng Zhang., “ Deep Learning Assisted Image Interactive Framework for Brain Image Segmentation” , (2020).

Guotai Wang, Maria A. Zuluaga, Wenqi Li, “DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation” , (2019).

Alessio Petrozziello; Christopher W. G. Redman, “Multimodal Convolu- tional Neural Networks to Detect Fetal Compromise During Labor and Delivery”( 2019).


Refbacks

  • There are currently no refbacks.