An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images

Abstract

In recent times,COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, image pre-processing is applied to enhance the quality of the image. Next, the CRNN model is applied as feature extraction and the hyperparameter tuning of CRNN takes place via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.

BEMD-3DCNN-based method for COVID-19 detection

“…Also we attempted to test the proposed architecture on two dataset with different sizes. To demonstrate the obtained results, we compared using the same metrics with state-of-the-art methods including Islam et al [ 5 ], Chowdhury et al [ 27 ], Rahimzade et al [ 41 ], Ucar et al [ 42 ], An et al [ 43 ], Ozturk et al [ 44 ], Punn et al [ 45 ], Narin et al [ 46 ], Ozcan et al [ 47 ], Bukhari et al [ 48 ], Mukherjee et al [ 49 ], Shankar et al [ 53 ], Yamaç et al [ 54 ], ZHOU et al [ 55 ], Tang et al [ 56 ], Narin et al [ 57 ], Ahsan et al [ 58 ], and Kaoutar Ben et al [ 59 ]. This section provides a description of the used datasets, experimental setup of the proposed deep-learning-based model, and also a discussion of the obtained results.…”

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

“…An algorithm called BMO-CRNN using chest X-ray images was proposed in the paper [ 56 ] as a smart COVID-19 diagnostic model that uses a barnacle mating optimization (BMO) algorithm and a cascaded recurrent neural network (CRNN) model. This approach uses a set of CRNN hyperparameters, such as learning rate, batch size, activation function and epoch count to find the best possible values of these parameters.…”

“…However, it is known that neural network models require a large amount of data in order to be trained and tested. In the studies [ 35 , 36 , 37 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 49 , 51 , 52 , 53 , 55 , 56 ], researchers have worked with a small number of chest X-ray data (75–6200 data) for training and testing purposes of COVID-19 detection and achieved varying accuracy (89.2–98%). However, due to the lack of proper training of the models primarily because of using such limited datasets, the credibility of the outcome is questionable.…”

Central hubs prediction for bio networks by directed hypergraph - GA with validation to COVID-19 PPI

“…Recently, many researchers aims to detect the COVID-19 through images of X-rays using the concept of Cascaded Recurrent Neural Network (CRNN) [12] and ultrasound X-rays by classifying them using Multi-layers Fusion [16] . Here, we aim to detect the influential nodes which gives a promising direction in the impact of current pandemic COVID-19 and in need of designing drugs.…”