Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat, Hanan; Sakr, George E.; Kilany, Rima

doi:10.1007/s00138-020-01101-5

Cited by 67 publications

(53 citation statements)

References 212 publications

(333 reference statements)

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“…According to these studies, the COVID-19 positive CXR images used in the experimentation came mostly from the set collected by Cohen [57], which contained 70 images of positive patients. The works [23,26,28] confirm this set of images available on GitHub 3 as the most used, followed by the sets available on Kaggle 2, . 4 In [25], works published in reliable databases such as IEEE explore, Web of Science, Science Direct, PubMed and Scopus are analyzed.…”

Section: Cxr and Ct In Ai Models For Covid-19 Classificationmentioning

confidence: 70%

“…In addition, to make a critical presentation, in the opinion of the authors of this work, of why most of this research leads to unreliable results. This is the main difference of our research with other review studies like [20,21,22,23,24,25,26,27,28,29,30] that analyze automatic classification of COVID-19 using CXR images, because none of them address the problems related to the lack of generalization reported in several papers [31]- [34].…”

Section: Introductionmentioning

confidence: 80%

“…Several are the advances reported in the scientific literature related to the automatic classification of CXR and CT images for the detection of COVID-19 [20,21,23]- [28]. These revision works constitute a starting point since they systematize the main knowledge achieved so far.…”

Section: Cxr and Ct In Ai Models For Covid-19 Classificationmentioning

confidence: 99%

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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

et al. 2021

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The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

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Section: Cxr and Ct In Ai Models For Covid-19 Classificationmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 80%

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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

et al. 2021

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“…However, this disease remains a public health problem. Consequently, research using computational methods capable of segmenting, detecting, and classifying pneumonia to support the medical community is recurrent in the literature [ 8 ].…”

Section: Related Workmentioning

confidence: 99%

A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system

Chagas

Rodrigues

Ivo

et al. 2021

J Real-Time Image Proc

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Pneumonia is responsible for high infant morbidity and mortality. This disease affects the small air sacs (alveoli) in the lung and requires prompt diagnosis and appropriate treatment. Chest X-rays are one of the most common tests used to detect pneumonia. In this work, we propose a real-time Internet of Things (IoT) system to detect pneumonia in chest X-ray images. The dataset used has 6000 chest X-ray images of children, and three medical specialists performed the validations. In this work, twelve different architectures of Convolutional Neural Networks (CNNs) trained on ImageNet were adapted to operate as the resource extractors. Subsequently, the CNNs were combined with consolidated learning methods, such as k-Nearest Neighbor (kNN), Naive Bayes, Random Forest, Multilayer Perceptron (MLP), and Support Vector Machine (SVM). The results showed that the VGG19 architecture with the SVM classifier using the RBF kernel was the best model to detect pneumonia in these chest radiographs. This combination reached 96.47%, 96.46%, and 96.46% for Accuracy, F1 score, and Precision values, respectively. Compared to other works in the literature, the proposed approach had better results for the metrics used. These results show that this approach for the detection of pneumonia in children using a real-time IoT system is efficient and is, therefore, a potential tool to aid in medical diagnoses. This approach will allow specialists to obtain faster and more accurate results and thus provide the appropriate treatment.

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“…Lastly, decision tree, linear discriminant, SVM and k-NN methods are used in the classification stage. Farhat et al 26 presented a literature review on state-of-the-art deep learning approaches in medical image analysis, produced between February 2017 and May 2020, highlighting several tasks such as classification, segmentation and identification, as well as various lung pathologies such as airway diseases, lung cancer, COVID-19 and other infections, was presented. Hussain et al 27 presented an overview of various approaches and methods of artificial intelligence, including neural networks, classical SVM and major state-of-the-art learning that can be applied to different forms of pandemics.…”

Section: Related Workmentioning

confidence: 99%

System for Quantitative Diagnosis of COVID-19-Associated Pneumonia Based on Superpixels With Deep Learning and Chest CT

Tetila¹,

Bressem²,

Astolfi³

et al. 2020

Preprint

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COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can lead to complications such as acute respiratory distress syndrome, acute heart injury and secondary infections in a relatively high proportion of patients and, consequently, significant mortality. The definitive diagnosis of COVID-19 is performed by real-time Polymerase Chain Reaction (RT-PCR). However, as the result of RT-PCR, at least for now, has been made available within a longer period of time than the computed tomography (CT) report, this has taken on an important role in the detection of patients infected with COVID-19. A rough estimate of the extent of lung involvement by the disease is also important and considered an additional criterion for deciding on discharge or hospitalization. Recent research has adopted deep neural networks and other machine learning approaches to detect the presence of lung infection caused by COVID-19. However, the extent of lung involvement (volume) caused by the disease has been little investigated. In this work, we created an end-to-end computer vision system to automatically quantify the Percentage Of Infection (POI) in chest CT images of COVID-19 cases confirmed by the laboratory. Trying to obtain high accuracy, we evaluated the performance of three deep neural networks well known in the literature, trained with three different training strategies: (1) no transfer learning with randomly initialized weights; (2) transfer learning without fine-tuning with ImageNet weights; and (3) transfer learning with 100% fine-tuning. Data augmentation and dropout were used during networks training to reduce overfitting and increase the generalization capacity of the models. Our approach consists of segmenting a chest CT with the SLIC Superpixels method and classifying each segment (superpixel) into a specific class (COVID or non-COVID). We used the weights of the deep neural network best evaluated for accuracy in our computer vision system in order to classify the superpixels in the image and quantify the regions of COVID-19 infection, thus calculating the POI on chest CT. The results indicate that deep learning models can be successfully used to support radiologists in the quantitative diagnosis of lung infection caused by COVID-19, reaching an accuracy of up to 98.4% with Inception-Resnet-v2 architecture.

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Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Cited by 67 publications

References 212 publications

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system

System for Quantitative Diagnosis of COVID-19-Associated Pneumonia Based on Superpixels With Deep Learning and Chest CT

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