Evolutionary multiobjective neural architecture search for organ medical image classification
Jin Yan, Huilin Liu, Kifayat Ullah · 2022
In medical clinical applications, we must not only ensure the accuracy of classification, but also ensure that the size of the network can be carried into hardware devices with limited computing resources. In this paper, we propose an evolutionary multiobjective neural architecture search (EMNAS) framework for organ image classification. In EMNAS, evolutionary algorithms are used to encode and search network structures, which can achieve more flexible hierarchical extraction of scene information from organ med-ical images. In addition, we utilize the evolutionary multi-objective optimization to search for the Pareto optimal solutions for two conflicting objectives of computational complexity and classification accuracy. Compared with many hand-designed classical convolutional neural networks and other well-known NAS algorithms, the effectiveness of EMNAS is proved by the fact that EMNAS can achieve the best results.