Blood Cell Image Classification Based on Improved EfficientNetB3 Model

Accurate classification of blood cells is very important for clinical diagnosis, but traditional classification methods are not only time-consuming, inefficient, but also easy to make subjective judgments. With the development of technology, neural network has made great progress in the field of blood cell analysis, especially deep learning technology has shown broad application prospects in the field of medical images. By using the lightweight EfficientNetB3 model to classify four kinds of cells, CBAM attention mechanism is added to the network to improve efficiency. The experimental results show that this method has excellent recognition ability and is helpful for the diagnosis and treatment of blood diseases in the future.