Automated COVID-19 Detection from Chest X-Ray Images: A High Resolution Network (HRNet) Approach

Ahmed, Sifat; Hossain, Tonmoy; Hoque, Oishee Bintey; Sarker, Sujan; Rahman, Sejuti; Shah, Faisal Muhammad

doi:10.1101/2020.08.26.20182311

Cited by 11 publications

(9 citation statements)

References 28 publications

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“…The main advantage of U-Net is that the location information from the downsampling path and the contextual information in the upsampling path are combined to get general information—containing context and localization, which is the key to predicting a better segmentation map. U-Net-based strategies were utilized in [ 12 – 14 , 17 , 18 , 38 , 40 , 61 , 62 , 66 , 73 , 74 , 76 , 77 , 80 , 81 , 94 , 95 ] for efficient and programmed lung segmentation extracting the lung region as the ROI.…”

Section: Methodologiesmentioning

confidence: 99%

A Comprehensive Survey of COVID-19 Detection Using Medical Images

et al. 2021

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The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.

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Section: Methodologiesmentioning

confidence: 99%

A Comprehensive Survey of COVID-19 Detection Using Medical Images

et al. 2021

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“…We have identified 19 such modifications and categorized them as miscellaneous UNet. The modifications were namely, (M1) UNet combined with CNN for feature extraction and Random Forest for ML classification [63,124]; UNet-based lung segmentation + feature extraction using high resolution network (HRNet) + FCN (Softmax) [55,63]; (M2) changes after the last decoder with Conv [54,162]; (M3) cascade of two plain UNet for segmentation [53,72,73,93,148]; (M4) cascade of two 3D UNet [42,53,96,114,141]; (M5) patch input to the conventional CNN [98,105,121]; (M6) feedback system to improve the training [58]; (M7) fusion of parametric (active contour model) curves with UNet for COVID-19 lesion segmentation [60];…”

Section: E Miscellaneous Variations In Unet By External Additionsmentioning

confidence: 99%

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Suri¹,

Bhagawati

Agarwal³

et al. 2023

IEEE Access

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Background & Motivation: Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. Methods: PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Findings and Conclusions: Vascular and non-vascular UNet systems dominated with sUNet classes with attention. The majority of the studies suffered from low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings. INDEX TERMS Image segmentation; vascular; non-vascular; UNet classes; UNet variations; UNetcomponents; explainable AI; pruning; bias.

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“…In practice many methods have been slow and costly, therefore automatic detection is required. Detection of COVID-19 from X-ray images has been performed by Apostolopoulos et al in [41]. They utilized two datasets with 1427 images and 1442 images.…”

Section: Related Workmentioning

confidence: 99%

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

et al. 2022

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In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to diagnose the virus from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

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Automated COVID-19 Detection from Chest X-Ray Images: A High Resolution Network (HRNet) Approach

Cited by 11 publications

References 28 publications

A Comprehensive Survey of COVID-19 Detection Using Medical Images

A Comprehensive Survey of COVID-19 Detection Using Medical Images

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

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