Deep residual network for three-dimensional (3-D) objects classification using phase-only digital holographic information

Abstract The binary classification of three-dimensional (3-D) objects for phase-only digital holographic information is performed using the various deep learning network models such as ResNet50, ResNet101, ResNet152, ResNet18, ResNet34, EfficientNetB0, DenseNet121, DenseNet169, Neural Architectural Search (NAS) Network, and InceptionV3. The four 3-D objects considered to perform binary classification are ‘triangle-square’, ‘circle-square’, ‘square-triangle’, and ‘triangle-circle’. The 3-D object ‘triangle-square’ has been considered for Class 1 and the remaining three 3-D objects have been considered for Class 2. The digital holograms of 3-D objects have been formed using the phase-shifting digital holographic (PSDH) technique and numerically reconstructed to obtain phase images. The phase image dataset consisting of 2,880 images was trained using all the various deep learning network models to obtain the results. The results such as loss/accuracy, loss/positive predictive value (PPV), and loss/sensitivity curves on the training/validation sets, error matrix, and performance metrics namely accuracy, PPV, sensitivity, F1-score, Matthews correlation coefficient (MCC), cohen_kappa_score (CKS), balanced_accuracy_score (BAS), jaccard_score (JS), log_loss (LL), hinge_loss (HL), and brier_score_loss (BSL) are shown for the binary classification task. Finally, the results such as receiver operating characteristic (ROC), and PPV-sensitivity curve are also shown to justify the performance of the work. The results obtained from the deep residual network models i.e. ResNet50, ResNet101, ResNet152, ResNet18, and ResNet34 were compared with other deep learning network models such as EfficientNetB0, DenseNet121, DenseNet169, NAS Network, and InceptionV3.