Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Abstract

Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

“…Another contribution in [ 15 ] developed a DL model based on Convolutional Neural Networks (CNNs) combined with Transfer Learning to predict COVID-19 from chest X-rays accurately. Furthermore, the Bidirectional LSTM network is also widely used in research; the authors in [ 16 ] also proposed ML approaches in detecting COVID-19 by chest X-ray images. They showed that the classification performance of the Bi-LSTM network on in-depth features gives the best results.…”

A Systematic Review on ICT-based Remote and Automatic COVID-19 Patient Monitoring and Care

“…A new method for identifying COVID-19 using X-ray images is proposed. Bi-LSTM network on the deep features [72] The study proposes a deep learning-based method for detecting Covid-19 and undetected cases using chest X-rays. Despite the small number of Covid-19 images in the dataset, the Bi-LSTM network outperformed the deep-neural net (DNN) with an accuracy of 97.6%.…”

Hybrid Deep Convolutional Generative Adversarial Network (DCGAN) and Xtreme Gradient Boost for X-ray Image Augmentation and Detection

“…These approaches are useful for doctors performing diagnosis and therapy. Keymal and Şen used automatic detection with a bidirectional LSTM network and deep features [8]. They relied on the Bi-LSTM network inside the deep feature and then compared it with the deep neural network of the fivefold cross-validation approach.…”

“…Their model was trained using 100 epochs and 32 batch sizes. The fourth section of their model was designed to handle the activation function, with two neurons representing the classes COVID-19 and no-finding [8]. A radiological study found a diagnostic tool for COVID-19.…”