White blood cells segmentation and classification using a random forest and residual networks implementation

Artificial intelligence algorithms are interesting solutions to automate the tedious manual counting of white blood cells by a specialist. Although interesting machine learning algorithms have been proposed for this task, there is a lack in the literature for high-accuracy methods (more than 99%) tested on larger datasets (more than 10 thousand images). This paper presents a segmentation and classification methodology, based on Random Forest and ResNet50, along with a comparison between ResNet models with different numbers of layers. The segmentation was tested in microscope-like images mounted using multiple single-cell images, widely available in online datasets, yielding 300×300 images to be classified by the residual network. For image classification, ResNet50 reached higher accuracies (99.3%, to the best of our knowledge, the higher accuracy for models with more than 1000 images), with the model size comparison pointing to model overfitting for larger models.