Fetal anomalies detection using convolutional neural networks

Abstract

Fetal anomalies are structural defects in a fetus that can lead to a complicated pregnancy, and disabilities later in life. Early detection and intervention are key in the prevention of later disabilities. Conventionally, specialists detect any anomalies in the fetus by physically analyzing the medical images such as ultrasound scans and MRIs. However, the cost of training a qualified radiologist and the general limitations of human beings such fatigue, lack of speed and experience may lead to delayed or erroneous diagnosis, hence delaying intervention. In the recent years machine learning has been applied in the detection of conditions such as pneumonia and cancer. This research processes the use of convolutional neural network in the detection of fetal anomalies from ultrasound scans. Due to time limitation, this research Focuses on the detection of only one fetal anomaly, Congenital Talipes Equinovarus (CTEV) which is one of the most common musculoskeletal defects that can be corrected by early detection and intervention. The objective of this study is to develop a deep learning model that can analyze ultrasound scans and detect Congenital Talipes Equinovarus. 200 samples of 2-dimensional ultrasound scans were used in the project, The sample size was split into three main sections: training, validation, and testing data. Three implementations of the model were done and compared: a standard CNN model without augmentation with 67.5% accuracy, a CNN model with augmentation with 77% accuracy and a CNN model with transfer learning with 85%. CNN model with transfer learning was selected to implement the model due to is high accuracy.