Computational Intelligence Conceptions to Automated Diagnosis: Feature Grouping for Performance Improvement

Abstract

The motivation of this work is to investigate two technological AI paths, evaluate the performance, and discuss the results. Using a covid-19 chest X-ray images databank, we address the two distinct experiments to this problem: (1) an investigation of feature extraction and classification using machine learning algorithms and (2) an approach based on transfer learning used in state-of-the-art applications. For the implementation of our proposal (1), an integrated framework consisting of 25 algorithms with different characteristics was developed to extract features from chest X-ray images. Following this path, we seek to focus on the spatial spectral signatures of shape, texture, local and global statistical quantities. The extraction of features based on information in Fourier and wavelet space-frequency domain was also implemented as part of the framework. On the other hand, several transfer learning CNN’s were also used to evaluate performance and to compere to the first technological path results. Furthermore, the performance of other results reported by various other works are provided. The comparative performance evaluation demonstrated that the two concepts for a computational intelligence tool can produce very good results even working in high-dimensional vector spaces.

Keywords:
feature extraction; machine learning; convolutional neural network; transfer learning; covid-19.

HIGHLIGHTS

• An integrated framework for feature extraction in medical images is proposed.

• It is investigated the automatic diagnosis in chest X-ray images using several algorithms.

• It is show that performance is increased when different feature extraction algorithms are merged.

• Experiments were performed with fourteen different IA algorithms and the results were compared.