Imaging Estimation for Liver Damage Using Automated Approach Based on Genetic Programming

Computer vision and image processing have become relevant in recent years due to their capabilities to support different tasks in several areas. Image classification, segmentation, and estimation are relevant issues addressed using various techniques. Imaging estimation is very important and helpful in biological applications. This work proposes a new approach for estimating the damages in the livers of the Wistar rats, using high-resolution RGB images. Instead of using invasive methods to determine the level of damage, the proposal allows us to measure the damage in the livers. The proposal is based on Genetic Programming (GP), the paradigm of evolutionary computing, which has become relevant in recent years for image-processing tasks. It provides flexibility, which allows the use of image processing functions to extract meaningful information from raw images. Furthermore, it allows the configuration of the regression model by performing a hyperparameter tuning to improve estimation performance. The approach includes a new set of functions through which the regression model is configured. Additionally, a set of functions is included to change the color spaces of the images to extract meaningful features from them. The results demonstrate the effectiveness of our approach when making the hyperparameter tuning and the efficiency in dealing with different color spaces, thus achieving the promised results when estimating according to the $R^2$, Mean Average Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) indicators. The proposed method achieves values higher than 0.5 of $R^2$ and lower than 0.51 of MSE, using different regression models. Additionally, the approach demonstrates that image preprocessing is necessary for improving the model’s performance, which is better than only using raw data where the values of RMSE are greater than 1.5. The lowest MSE value of our proposed method was 0.51, outperforming the methods without preprocessing.