COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

Abstract

The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.

Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-ray Images

“…reduced false negative rates) along with other metrics. Our proposed CNN model shows better specificity (97.01%) than many existing works (Abbas et al, 2020; Apostolopoulos & Mpesiana, 2020; Minaee et al, 2020; Chandra et al, 2021; Duran-Lopez et al, 2020; Pandit & Banday, 2020). At the application level, the usage of our model may prevent COVID-19 community transmission by detecting false negative cases more accurately.…”

“…Moura et al (2020) investigated 1616 chest X-ray images using DenseNet161 where it shows 79.89% accuracy to classify normal, pathological and COVID-19 patients. Duran-Lopez et al (2020) represented a custom CNN based model named COVID-XNet that shows 94.43% average accuracy, 98.8% AUC, 96.33% sensitivity, and 93.76% specificity respectively. Shorfuzzaman & Hossain (2020) provided a siamese neural network called MetaCOVID to integrate contrastive learning with a fine-tuned pre-trained ConvNet encoder and capture unbiased feature representations using 10-shot learning scores and compared among the meta learning algorithm with InceptionV3, Xception, Inception, ResNetV2, and VGG16.…”

Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

“…There have been multiple works done by researchers in the area of COVID-19 patient detection using CXR images [4,5,7,12,[26][27][28][29][30][31][32]. In one such work by Makris et al [4], transfer learning has been used with the Inception-v3 network to classify normal, pneumonia and COVID-19 patients using CXR images.…”

AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

“…Clearly, the application of these technologies in the field of e-Health, both in the analysis of physiological signals and as diagnostic aid systems with medical images, has an enormous impact and helps to significantly reduce the workload of healthcare professionals. Several works related to this area can be found [ 25 , 26 , 27 , 28 ], and currently we can even find some interesting applications related to the detection of COVID-19 [ 29 , 30 ]. Due to the rise of these systems, it is interesting to carry out a preliminary dataset study using DL techniques in order to evaluate its quality.…”