The measurability analysis of abnormality detection based on DOT method using stack auto-encoding neural network model

The stacked autoencoder (SAE) neural network applied to diffuse optical tomography (DOT) achieves accurate and stable detection of the position and size of tissue abnormality. The quality of modeling data influences the robustness and the accuracy of the model, the measurability of the model determines the effective range of the data cleaning method used in clinical practice. In order to determine the effective range of this method in clinical use, we analyze the measurability of anomaly detection based on DOT method. The analysis result is used as a priori information to clean the neural network sample data set used in this work. The results show that excluding the data outside the measurable range, the proposed method enables the network to achieve a prediction accuracy of 99% within the measurable range and achieves rapid and accurate detection of the position and size of abnormality in the tissue.