Training Deep Neural Networks with Multi-Objective Adam Optimizer for Medical Image Classification

The multi-objective Adam (MAdam) is a populationbased optimizer that integrates Adam exploitation with evolutionary algorithm exploration. The capability of the MAdam optimizer and its opposition-based extension (OMAdam) to provide a wide range of solutions was previously examined in training neural networks for binary classification. This study investigates the practical applications of these two optimizers in medical image classification, developing two novel diagnostic techniques based on the MNIST and real medical image datasets MedMNIST. For both optimizers, their functionality was demonstrated by generating a set of Pareto fronts comprised of corresponding model parameters in the decision space. In the first approach, false positives and false negatives in binary cross entropy were assigned as two conflicting loss functions. The optimized weight configuration of the corresponding singleobjective Adam optimizer was captured by the set of solutions along the spectrum of the Pareto fronts. MAdam and OMAdam optimizers achieve F1-scores comparable to those of the singleobjective Adam optimizer. In the second approach, by aligning conflicting performance metrics as differentiable loss functions in MAdam, the obtained Pareto front is a direct representation of the performance metrics space, including precision, sensitivity, and specificity.