Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Abstract

Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued. As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases. To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 0.02 and a sensitivity of 0.987 0.05, 0.963 0.05, and 0.973 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In su...

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

“…However, the investigation is primarily focussed on a single case, i.e., COVID-19 CT image data. Rajpal, Lakhyani, Singh, Kohli, and Kumar (2021) have used a deep convolutional neural network (ResNet-50) to learn the features from the chest X-ray images and distinguished among the three classes, namely, normal, COVID-19 and pneumonia. Gungor (2021) de-noised the CT images through DWT using various mother wavelets and diagnosed the COVID-19 disease.…”

Deep learning applied to automatic disease detection using chest X‐rays

“…Adding processes that improve feature selection also improved performance, including correlation‐based feature selection, 60 feature categorisation with decision trees, 61,62 SVMs 63 and even handpicking features 64 …”

“…58,59 Adding processes that improve feature selection also improved performance, including correlation-based feature selection, 60 feature categorisation with decision trees, 61,62 SVMs 63 and even handpicking features. 64 Other techniques were also explored including a DNN model that utilises depth wise convolution with varying dilation rates, 65 residual neural networks (ResNets), 66,67 YOLO real-time DNN object detection systems, 68 Capsule Network-based (CapsNet) frameworks 69,70 and training DNN models from scratch. 71 Multinetwork models proved effective including multilevel pipelines based on DL models 72 and the incorporation of shallow 3D CNNs.…”

A stacked ensemble for the detection of COVID-19 with high recall and accuracy

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