SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

Abstract

COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.

A deep learning-based COVID-19 classification from chest X-ray image: case study

“…This research adopts an augmentation technique without any loss to increase as well as maintain the dataset. To increase the performance of deep learning model we need more data, so augmentation technique is used to increase the dataset and performance accuracy [ 12 ]. Ten times the dataset is increased by augmentation using the following algorithm.…”

“…Thus, the bias in small datasets and the lack of representative training and tuning data impair the performance of deep learning models. A simple way to deal with these overfitting challenges data augmentation technique is used [ 12 ]. Several experiments were conducted using chest X-ray for COVID-19 classification and the results demonstrate that the data augmentation strategy improves the performance of COVID-19 classification [ 13 ].…”

CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images

“…Muhammad et al. [35] introduce a feature augmentation mechanism using reconstruction independent component analysis (RICA) to deal with the lack of sufficient labeled data available. A CNN-BiLSTM method was proposed for the detection of Covid-19.…”

Deep learning models-based CT-scan image classification for automated screening of COVID-19

“…The majority of coronaviruses are not very harmful. SARS-CoV-2 is a new variant of coronavirus that has never been detected earlier in humans [1] . This virus was found at the end of 2019 in the Wuhan province of China and critically affects the lungs of an infected person [2] .…”