Local receptive field based extreme learning machine with three channels for histopathological image classification

The classification of histopathological images is a challenging task in the study of real-life medicine owing to the diverse geometrical structures and different histology features. This paper proposes a framework called Local Receptive Field based Extreme Learning Machine with Three Channels (3C-LRF-ELM), which can automatically extract histopathological features to diagnose whether there is a disease. We conduct experiments on the real-world image dataset that consists of mammalian lung, kidney and spleen organ images provided by the animal diagnostics lab (ADL) Pennsylvania State University. The training sets are consisted of overlapping blocks which are randomly extracted from arbitrary 40 images of each type image in the ADL dataset. The remaining images are equally divided into 850 blocks, and then they are given to the model 3C-LRF-ELM to generate the labels. The final label of each image is defined by the optimal threshold $$\alpha$$α. The 3C-LRF-ELM can be single layer network or multi-layer network. In this paper, considering the computational complexity, we choose the single layer 3C-LRF-ELM and two layers 3C-LRF-ELM structure to analyze the influence of the number of layers on the experimental results. The experimental results show that the single layer 3C-LRF-ELM structure is better than two layers 3C-LRF-ELM. Compared to the Discriminative Feature-oriented Dictionary Learning, the single layer 3C-LRF-ELM has a better classification performance.