Design and Compression Study for Convolutional Neural Networks Based on Evolutionary Optimization for Thoracic X-Ray Image Classification

Computer Vision has lately shown progress in addressing a variety of complex health care difficulties and has the potential to aid in the battle against certain lung illnesses, including COVID-19. Indeed, chest X-rays are one of the most commonly performed radiological techniques for diagnosing a range of lung diseases. Therefore, deep learning researchers have suggested that computer-aided diagnostic systems be built using deep learning methods. In fact, there are several CNN structures described in the literature. However, there are no guidelines for designing and compressing a specific architecture for a specific purpose; thus, such design remains highly subjective and heavily dependent on data scientists' knowledge and expertise. While deep convolutional neural networks have lately shown their ability to perform well in classification and dimension reduction tasks, the challenge of parameter selection is critical for these networks. However, since a CNN has a high number of parameters, its implementation in storage devices is difficult. This is due to the fact that the search space grows exponentially in size as the number of layers increases, and the large number of parameters necessitates extensive computation and storage, making it impractical for use on low-capacity devices. Motivated by these observations, we propose an automated method for CNN design and compression based on an evolutionary algorithm (EA) for X-Ray image classification that is capable of classifying radiography images and detecting possible chest abnormalities and infections, including COVID-19.Our evolutionary method is validated through a series of comparative experiments against relevant state-of-the-art architectures.