Applied Enhanced Q-NAS for COVID-19 Detection in CT Images

ABSTRACT

The Deep Neural Networks are flexible and robust models that have gained attention from the machine learning community over the last decade. During the construction of a neural network, an expert can spend significant time designing a neural architecture with trial and error sessions. Because of the manual process, there is a greater interest in Neural Architecture Search (NAS), which is an automated method of architectural search in neural networks. Quantum-inspired evolutionary algorithms present propitious results regarding faster convergence when compared to other solutions with restricted search space and high computational costs. In this work, we enhance the Q-NAS model a quantum-inspired algorithm to search for deep networks by assembling substructures. We present a new architecture that was designed automatically by the Q-NAS and applied to a case study for COVID-19 vs. healthy classification. For this classification, the Q-NAS algorithm was able to find a network architecture with only 1.23 M parameters that reached the accuracy of 99.44%, which overcame benchmark networks like Inception (GoogleLeNet), EfficientNet and VGG that were also tested in this work. The algorithm is publicly avaiable at https//github.com/julianoce/qnas. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.