CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization

Abstract

With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumonia detection schemes are proposed utilizing a small amount of COVID-19 chest X-rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. Since the chest Xray images corresponding to COVID-19 caused pneumonia and other traditional pneumonias have significant similarities, at first, a large number of chest X-rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X-ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient-based discriminative localization is integrated to distinguish the abnormal regions of X-ray images referring to different types of pneumonia. Extensive experimentations using two different datasets provide very satisfactory detection performance with accuracy of 97.4% for COVID/Normal, 96.9% for COVID/Viral pneumonia, 94.7% for COVID/Bacterial pneumonia, and 90.2% for multiclass COVID/normal/Viral/Bacterial pneumonias. Hence, the proposed schemes can serve as an efficient tool in the current state of COVID-19 pandemic. All the architectures are made publicly available at: https: //github.com/Perceptron21/CovXNet.

Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

“…With regards to the classi cation of COVID-19 and normal CXR images, it can be observed that our model provides signi cantly a better performance compare to studies that utilized small amount of dataset such as Mahmud et al, 2020 [34] and models developed from scratch. The impressive performance of the model is attributed to the use of TL based on pretrained models which have shown to perform e ciently with less amount of data compare to models designed from scratch such as Tan et al, 2018 [19].…”

“…The models achieved higher accuracies, sensitivities and speci cities. A multi dilation CNN is utilized by Mahmud et al, 2020[34] to classify COVID-19 and other forms of pneumonia. The study utilized a deep CNN as COVXNet with modi cations base on varying dilation rates for feature extraction, optimization, stacking algorithms and gradient-based discriminative localization to train dataset containing 1493 Non-COVID-19 viral pneumonia, 305 COVID-19 pneumonia, 2780 bacterial pneumonia.…”

A Complete Survey on Automatically Diagnosing COVID-19 In the field of Computer Vision and A Collection of Medical Images

“…On the contrary, some papers apparently stated the size of the train-test-validation data of COVID-19 images [16], [20], [22] and for those papers, I computed the ratio according to the split that was provided. Even so, for some papers, the distribution of the dataset [7], [10], [26] was not clearly mentioned. Moreover, some papers apparently mentioned the use of data for validation [1], [16], [25], [30] along with some papers do not state exact data instead provide a comparison based on 10-fold cross-validation [6], 5-fold cross-validation [3,[18] for performance assessment.…”

“…Moreover, some researchers also use an adaptive winner filter for noise reduction [59], Affine Transformation [31] in their research. [2], [5], [6], [8], [10][11], [16], [18], [19], [20], [22], [23] [24],[ [26], [28], [ 30], [32], [37],43], [45], [46], [48], [50], [53], [54], [56], [60], [61], [63], [67] 31…”

“…CovXNet is a multi-dilation convolutional neural network which detects COVID-19 and other pneumonia automatically using chest X-ray images that utilize depthwise convolution for efficiently extracting diversified features. In the paper [26] author uses a total of 6161 datasets among which the first dataset consists of 5856 images (1583 normal X-rays, 1493 non-COVID viral pneumonia X-rays, and 2780 bacterial pneumonia X-rays) and the 2nd dataset comprises 305 images. CoroNet is a Deep Convolutional Neural Network model that is based on Xception architecture.…”