Abstract
This paper proposes a novel deep learning-based framework for the multi-class prediction analysis of 3-D objects through the application of phase-only digital holographic data obtained via the phase-shifting approach. The dataset utilised in this study comprises seven distinct 3-D object pairings: M-L, D-L, C-N, A-I, D-S, C-S, and H-R, all represented by phase-only holographic images that maintain crucial spatial and depth information. The digital holograms were formed using programmatically generated synthetic 3-D objects and further numerically processed to create 2-D phase images that served as inputs to the prediction task. A custom convolutional neural network (CNN) architecture, along with a modified AlexNet architecture, was employed to simultaneously predict multiple continuous attributes associated with the 3-D objects from their respective phase-only inputs. Regression (Prediction) task model performance was evaluated using mean squared error (MSE), mean absolute error (MAE), and R2 score metrics, demonstrating the ability to perform multi-class prediction with high accuracy and robustness, while also being computationally efficient. The CNN has achieved better regression performance compared to AlexNet in terms of MSE, MAE, and R2 score values. The use of deep learning in this manner thus provides a scalable method of analysing 3-D objects using holographic imaging techniques, moving away from previous binary regression techniques and the limitations of traditional machine learning approaches towards richer predictions of multiple associated attributes.
