Contour-aware multi-label chest X-ray organ segmentation

Kholiavchenko, Maksym; Sirazitdinov, Ilyas; Kubrak, Konstantin; Badrutdinova, R.; Kuleev, Ramil; Yuan, Yixuan; Vrtovec, Tomaž; Ibragimov, Bulat

doi:10.1007/s11548-019-02115-9

Cited by 46 publications

(27 citation statements)

References 42 publications

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“…While U-Net relies on concatenation in the decoder, LinkNet [29] adds the information from the encoder and has also been used for medical image segmentation [30,31] including polyp segmentation [32]. Furthermore, these two networks are usually compared in studies from different medical fields [33][34][35][36]. For polyp segmentation on colonoscopy images, different approaches can be found.…”

Section: Introductionmentioning

confidence: 99%

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

et al. 2020

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Colorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.

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Section: Introductionmentioning

confidence: 99%

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

et al. 2020

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“…Digital image processing technology has been widely applied in the medical field, including organ segmentation and image enhancement and repair, providing Initial support for subsequent diagnosis [13,14]. With the rapid development of Artificial Intelligence (AI), Deep learning techniques associated with automatic diagnosis in the medical field have spread widely, as they have become a useful tool for medical specialists.…”

Section: Introductionmentioning

confidence: 99%

CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

Al-Bawi

Al-Kaabi

Jeryo

et al. 2020

Preprint

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Purpose: COVID-19 pandemic continues to hit countries one after the other and has dramatically affected the health and well-being of the world's population. With the daily increase in the number of people with this disease, the impressive speed of spread and the delay in the results of PCR analysis, it may cause the disease to spread more broadly. Therefore it is necessary to consider finding alternative methods of detection and diagnosis COVID-19 to prohibit the spread of the disease among people. Convolutional Neural Network (CNN) automated detection systems have shown auspicious results in detecting patients with COVID-19 through radiography; thus, we suggest them as an alternative option to diagnose COVID-19.Method: In this study, an early screening model based on the enhancement of classical Visual Geometry Group Network (VGG) with Convolutional Covid Block (CCBlock) was proposed to detect and distinguish COVID-19 from Pneumonia, and healthy people using chest X-ray radiographs. The data set used for model testing is the x-ray images available on public platforms, which consist of 1,828 x-ray images, including 310 images for confirmed COVID-19 patients, 864 images for pneumonia patients, and 654 images for healthy people.Results: The experiment result of the dataset showed that the added enhancements to the classical VGG network with X-ray imaging provide the highest detection performance and overall accuracy of 98.52% for two classes and 95.34% accuracy for three classes.Conclusions: Considering the achievement results obtained, it was found that utilizing the enhanced VGG deep neural network helps radiologists automatically diagnose COVID-19 in X-ray images.

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“…The technology of digital image processing has widely been used for medical purposes such as organ segmentation as well as image enhancement and repair to provide the initial support for any subsequent diagnosis (Kholiavchenko et al 2020 ; Patel et al 2020 ). With the rapid development of artificial intelligence (AI), deep learning techniques of automated medical diagnoses have become widely popular with specialists.…”

Section: Introductionmentioning

confidence: 99%

CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images

et al. 2020

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Propose Troubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world’s population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis. Materials and methods Based on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block ( CCBlock ), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. The model testing dataset included 1828 X-ray images available on public platforms. Three hundred and ten images were showing confirmed COVID-19 cases, 864 images indicating pneumonia cases, and 654 images showing healthy people. Results According to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes. Conclusions According to the results, using the enhanced VGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography.

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Contour-aware multi-label chest X-ray organ segmentation

Cited by 46 publications

References 42 publications

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images

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