Blood cells classification using embedded machine learning / Zhang Zimu - Zhang, Zimu (2021)
With the development of science and technology, digital image processing has been applied to various fields, especially playing an important role in medicine. This thesis mainly studies the identification of blood cells in complex situations, and proposes a YOLOv3 target detection method. The ResNet network is used to optimize the Darknet- 53 feature extraction structure of YOLOv3, and the feature pyramid network is used to obtain the four scale features of the target to fuse the shallow features and deep feature information. Then adjust the influence weight of the loss function according to the size of the detected target, so as to enhance the detection effect of small targets and mutual occluded objects. The experimental results on the data set show that the detection accuracy of the YOLOv3 method can reach 83.74%,and made a graphical interface with Python QT5.