Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients

Štifanić, Daniel; Musulin, Jelena; Jurilj, Zdravko; Šegota, Sandi Baressi; Lorencin, Ivan; Anđelić, Nikola; Vlahinić, Saša; Šušteršič, Tijana; Blagojević, Anđela; Filipović, Nenad; Car, Zlatan

doi:10.4108/eai.7-7-2021.170287

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…However, the SSM developed in this study had a higher mIoU value than in a previous study [15] using DeepLabv3+, similar to our method. Considering this finding, we believe that the semantic segmentation and data augmentation methods used in our method are appropriate.…”

Section: Discussionsupporting

confidence: 59%

“…In the field of CXR images, the classification, semantic segmentation, and object detection technologies have been used for lesion classification [10], the semantic segmentation of the lung field areas [11,12], and the detection of diseases in lung field areas [13], respectively. In addition, with the recent global coronavirus disease 2019 pandemic, these technologies can be applied to detect the presence of pneumonia and segment the pneumonia in the lung field area [14,15]. The development of these technologies has led to the development of the technology.…”

Section: Introductionmentioning

confidence: 99%

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Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

et al. 2023

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Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist’s eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need for a retake, which is expected to further improve examination throughput. In this study, we developed software for evaluating chest X-ray images to determine whether a repeat radiographic examination is necessary, based on the combined application of DL technologies, and evaluated its accuracy. The target population was 4809 chest images from a public database. Three classification models (CLMs) for lung field defects, obstacle shadows, and the location of obstacle shadows and a semantic segmentation model (SSM) for the lung field regions were developed using a fivefold cross validation. The CLM was evaluated using the overall accuracy in the confusion matrix, the SSM was evaluated using the mean intersection over union (mIoU), and the DL technology-combined software was evaluated using the total response time on this software (RT) per image for each model. The results of each CLM with respect to lung field defects, obstacle shadows, and obstacle shadow location were 89.8%, 91.7%, and 91.2%, respectively. The mIoU of the SSM was 0.920, and the software RT was 3.64 × 10−2 s. These results indicate that the software can immediately and accurately determine whether a chest image needs to be re-scanned.

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Section: Discussionsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

et al. 2023

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“…People with congenital diseases, especially lung disease, are at high risk of experiencing COVID-19, because it can reduce lung capacity and decrease saturation. [3], [4]. Rapid and accurate screening and early diagnosis of patients play an important role in controlling outbreaks and reducing the spread of this disease [5].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications

Rudiansyah

2023

ComEngApp

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COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.

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“…Globally engaged researchers [20] are expediting the development of vaccines and treatments and searching for new diagnostic methods. Blood testing, virus tests, and medical imaging [13] are standard diagnostic procedures in the United States. Blood tests can identify antibodies against the coronavirus-2 (SARS-CoV-2) that causes severe acute respiratory syndrome (SARS).…”

Section: Introductionmentioning

confidence: 99%

MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization

Deepak

Madiajagan

Kulkarni

et al. 2023

XST

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BACKGROUND: COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed. OBJECTIVE: The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images. METHODS: Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes. RESULTS: The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches. CONCLUSION: The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients.

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Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients

Cited by 7 publications

References 28 publications

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications

MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization

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