Optimized deterministic multikernel extreme learning machine for classification of COVID-19 chest Xray images

In this paper, a novel technique has been proposed to exploit the capability of residual network (ResNet) deep learning model to extract the features. It is utilized neither in pretrained form nor as a transfer learning model. ResNet uses shortcut connections to create shortcut blocks in order to skip blocks of convolutional layers (residual blocks). These stacked residual blocks significantly increase training effectiveness and address the degradation issue. For the purpose of classification, a multiple kernel learning based deterministic extreme learning machine (MKD-ELM) which uses a linear combination of different base kernels as target kernel function is designed to classify chest Xray images. Multiple kernels are used here to exploit their non-linear mapping capability on heterogeneous data. MKD-ELM is an enhanced classifier, which does not require iterative training of its parameters. The proposed technique has better feature extraction along with non-iterative training, thus it is having very fast training and very good generalization performance. The kernel and regularization parameters that influence how accurate MKD-ELM is at classifying data, are tuned through experimentation. So, an optimization technique called the genetic algorithm (GA) has been utilized to determine the ideal combination of these parameters for improved performance. The performance of the proposed technique is analysed for COVID-19 detection problem using chest Xray (ChXR) images by changing the training set, types of kernels and coefficients used for combining base kernels. The proposed algorithm achieves a 97.27% recognition rate on first dataset which comprises 5,856 images and 99.06% on the second dataset which consists of 13,808 images. A higher recognition rate is attained for these ChXR image datasets, in respect to modern techniques demonstrating the effectiveness of the proposed algorithm.