XMIAR: X-ray Medical Image Annotation and Retrieval

Abdulrazzaq, M. M.; Yaseen, Imad Fakhri Taha; Noah, Shahrul Azman Mohd; Fadhil, Moayad A.; Ashour, Mohammed Waleed

doi:10.1007/978-3-030-17798-0_51

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Usually the classifiers are focused in the minimization of the global error rate and thus, when dealing with imbalanced datasets, the algorithms tend to benefit the most frequent classes (known as majority classes). Nevertheless, depending on the problem, the main interest of the task could be on properly labeling the rare patterns, i.e., the less frequent classes (known as minority ctlasses), such as in credit card fraud detection [18] and medical image classification [19][20][21] .…”

Section: Imbalanceness Data and Resamplingmentioning

confidence: 99%

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

Pereira

Bertolini

Teixeira

et al. 2020

Computer Methods and Programs in Biomedicine

436

302

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Background and Objective:The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. Early diagnosis is crucial for correct treatment in order to possibly reduce the stress in the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. Although CT scan is the gold standard, CXR are still useful because it is cheaper, faster and more widespread. This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. Methods:In order to achieve the objectives, we have proposed a classification schema considering the following perspectives: i) a multi-class classification; ii) hierarchical classification, since pneumonia can be structured as a hierarchy. Given the natural data imbalance in this domain, we also proposed the use of resampling algorithms in the schema in order to re-balance the classes distribution. We observed that, texture is one of the main visual attributes of CXR images, our classification schema extract features using some well-known texture descriptors and also using a pre-trained CNN model. We also explored early and late fusion techniques in the schema in order to leverage the strength of multiple texture descriptors and base classifiers at once.To evaluate the approach, we composed a database, named RYDLS-20, containing CXR images of pneumonia caused by different pathogens as well as CXR images of healthy lungs. The classes distribution follows a real-world scenario in which some pathogens are more common than others. Results:The proposed approach tested in RYDLS-20 achieved a macro-avg F1-Score of 0.65 using a multiclass approach and a F1-Score of 0.89 for the COVID-19 identification in the hierarchical classification scenario. Conclusions:As far as we know, the top identification rate obtained in this paper is the best nominal rate obtained for COVID-19 identification in an unbalanced environment with more than three classes. We must also highlight the novel proposed hierarchical classification approach for this task, which considers the types of pneumonia caused by the different pathogens and lead us to the best COVID-19 recognition rate obtained here.

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Section: Imbalanceness Data and Resamplingmentioning

confidence: 99%

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

Pereira

Bertolini

Teixeira

et al. 2020

Computer Methods and Programs in Biomedicine

436

302

View full text Add to dashboard Cite

show abstract

“…However, classifier focus on minimizing the global error rate, thus the algorithm concentrates on the majority classes, but, it also focuses on minority classes based on the problem domain such as medical image classification and credit card fraud detection [18,19]. In the real world, classifying pneumonia type using CXR images is also considered as imbalanced learning as there are only a few people with affected pneumonia than considering health persons [20,21]. On the other hand, the number of people affected by various types of pneumonia disease is also imbalance [22,23].…”

Section: Imbalanceness Data and Resamplingmentioning

confidence: 99%

Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN)

et al. 2020

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Rapid spread of Coronavirus disease COVID-19 leads to severe pneumonia and it is estimated to create a high impact on the healthcare system. An urgent need for early diagnosis is required for precise treatment, which in turn reduces the pressure in the health care system. Some of the standard image diagnosis available is Computed Tomography (CT) scan and Chest X-Ray (CXR). Even though a CT scan is considered a gold standard in diagnosis, CXR is most widely used due to widespread, faster, and cheaper. This study aims to provide a solution for identifying pneumonia due to COVID-19 and healthy lungs (normal person) using CXR images. One of the remarkable methods used for extracting a high dimensional feature from medical images is the Deep learning method. In this research, the state-of-the-art techniques used is Genetic Deep Learning Convolutional Neural Network (GDCNN). It is trained from the scratch for extracting features for classifying them between COVID-19 and normal images. A dataset consisting of more than 5000 CXR image samples is used for classifying pneumonia, normal and other pneumonia diseases. Training a GDCNN from scratch proves that, the proposed method performs better compared to other transfer learning techniques. Classification accuracy of 98.84%, the precision of 93%, the sensitivity of 100%, and specificity of 97.0% in COVID-19 prediction is achieved. Top classification accuracy obtained in this research reveals the best nominal rate in the identification of COVID-19 disease prediction in an unbalanced environment. The novel model proposed for classification proves to be better than the existing models such as ReseNet18, ReseNet50, Squeezenet, DenseNet-121, and Visual Geometry Group (VGG16).

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Analyzing the Impact of Resampling Approaches on Chest X-Ray Images for COVID-19 Identification in a Local Hierarchical Classification Scenario

Barros

Selleti

Queiroz

et al. 2021

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)

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XMIAR: X-ray Medical Image Annotation and Retrieval

Cited by 3 publications

References 21 publications

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN)

Analyzing the Impact of Resampling Approaches on Chest X-Ray Images for COVID-19 Identification in a Local Hierarchical Classification Scenario

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