  The neural network needs excessive costs of time because of the complexity of
architecture when trained on images. Transfer learning and fine-tuning can help
improve time and cost efficiency when training a neural network. Yet, Transfer
learning and fine-tuning needs a lot of experiment to try with. Therefore, a
method to find the best architecture for transfer learning and fine-tuning is
needed. To overcome this problem, neuro-evolution using a genetic algorithm can
be used to find the best architecture for transfer learning. To check the
performance of this study, dataset ChestX-Ray 14 and DenseNet-121 as a base
neural network model are used. This study used the AUC score, differences in
execution time for training, and McNemar's test to the significance test. In
terms of result, this study got a 5% difference in the AUC score, 3 % faster in
terms of execution time, and significance in most of the disease detection.
Finally, this study gives a concrete summary of how neuro-evolution transfer
learning can help in terms of transfer learning and fine-tuning.
