Combination of Aggregated Channel Features (ACF) Detector and Faster R-CNN to Improve Object Detection Performance in Fetal Ultrasound Images

The research proposed a method that combined non-deep learning detector that called Aggregated Channel Features (ACF) detector and Convolutional Neural Network (CNN) that named Faster R-CNN detector to extract a cross-sectional area of the fetal limb in an ultrasound image. This combination is appropriate to solve the problem of object detection where the object has no clear characteristic, it has shape variation, blurred, and no clear boundaries, which is difficult to solve using the common thresholding or the edge detection method. This method also deals with the ultrasound image analysis which the training set is small. The pre-trained CNN can establish the classification model from the small annotated training data. ACF detector provides the region proposals of the non-cross-sectional area as an input of pre-trained CNN. The proposed method could improve the average precision of detection result when it was compared with Faster R-CNN and ACF detector alone. Also, the combination method could reduce the elapsed time of the Faster R-CNN training phase significantly.