An Effective Cell Pathology Image Detection Method Based on Deep Stacked Auto-Encoder Combined with Random Forest

Abstract

Cytopathology mainly studies the causes and pathogenesis of disease occurrence, as well as the changes in the physiological functions of cells during the process of disease occurrence, providing a basis for the prevention and diagnosis of diseases. Using computational simulation technology to accurately detect cell pathology images can effectively avoid the influence of subjective factors in diagnostic operation by pathologist, and provide reliable technical means for case diagnosis. In this study, we design an effective model to detect cell pathology images based on deep stacked auto-encoder algorithm. Firstly, the cell pathology images are preprocessed by standardization. Secondly, the processed images are sent to the stacked auto-encoder algorithm to extract theirs hidden feature information. Finally, the random forest algorithm is used to quickly and accurately detect theirs category. We introduce cross-validation in our experiments to test the robustness and stability of the results. In addition, we also compared the model based on a k-nearest neighbor classifier and achieved good results. These excellent experiment results indicated that the proposed method can accurately detect cell pathology images and can be used as a reliable tool for pathologists to diagnose rapidly.