Segmentation of Chest X-Ray Images Using U-Net Model

Kamil, Mohammed Y.; Hashem, Sahar A.

doi:10.13164/mendel.2022.2.049

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…ADAM was employed as an optimization algorithm because it involves the advantages of RMSProp and AdaGrad. The experimental results of the proposed model were in terms of accuracy 91.47 and IoU 74.94% [24].…”

Section: B Related Workmentioning

confidence: 97%

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Hasan,

Abdulazeez

2024

ijcs

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As a result of technological advancements, a variety of medical diagnostic systems have grown rapidly to support the healthcare sectors. Over the past years, there has been considerable interest in utilizing deep learning algorithms for the proactive diagnosis of multiple diseases. In most cases, Coronavirus (COVID-19) and tuberculosis (TB) are diagnosed through the examination of pulmonary X-rays. Deep learning algorithms can identify tuberculosis with an almost medical-grade level of consistency by extracting the lung regions in the X-ray images. The probability of tuberculosis detection is increased when classification algorithms are applied to segmented lungs rather than the entire X-ray. The main focus of this paper is to execute lung segmentation from X-ray images using the deeplabv3plus CNN-based semantic segmentation model. In other CNN architectures, the feature resolution diminishes as the network becomes deeper due to the use of sequential convolutions with pooling or striding within the down-sampling stage. To tackle this drawback, deeplabv3plus incorporates "Atrous Convolution" in addition to modifying the pooling and convolutional striding components of the backbone. The experimental results were: an accuracy of 97.42%, a Jaccard index of 93.49%, and a dice coefficient of 96.63%. We also conduct an extensive comparison between the deeplabv3plus segmentation model and other benchmark segmentation architectures. The results prove the ability of the deeplabv3plus model to achieve precise lung segmentation from X-ray images.

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Section: B Related Workmentioning

confidence: 97%

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Hasan,

Abdulazeez

2024

ijcs

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“…The U-Net is the most demanding and widely used semantic segmentation framework for a CNN. The U-Net model was utilized by the authors (Kamil, & Hashem, 2022;Kumarasinghe et al, 2022;Lu et al, 2021;Rahman et al, 2021b;Munawaret al, 2020;Liu et al, 2020) for segmentation purpose. Ma, and Lv (2022) Swin transformer backbone network is applied to the application model of X-ray identification and analysis, and the system is optimized according to the CXR characteristics.…”

Section: Pneumoniamentioning

confidence: 99%

A systematic survey on deep learning techniques for chest disease detection using chest radiographs

Soni

Rai

2023

JCST

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The lung is one of the most sensitive internal organs of the human body that gets infected by constant exposure to chemicals, particles, and infectious organisms in the atmospheric air. Due to this, the most dangerous chest diseases take place which are the leading cause of human disability and death throughout the world. These pulmonary diseases can be recognized by medical imaging techniques i.e. Chest radiography, Computed tomography, Pulmonary and bronchial angiography, Magnetic resonance imaging, Ultrasonography, and Nuclear medicine techniques. A massive amount of chest reports are generated every day that contain a large amount of anatomical and potentially pathological information, but manual detection and classification of chest abnormalities are considered a tedious and time-taking task. In addition, it also requires skilled radiologists as these reports are often difficult to read and differentiate the pathologies of the chest. To eradicate this issue and give a value-added solution; artificial intelligence and deep learning-based algorithms show excellent performance and have proven their effectiveness for object recognition and image segmentation. The primary aim of this study is to present a comprehensive analysis of the deep learning approaches used to identify various types of pulmonary pathologies on CXRs. In addition, we provide a detailed analysis of the most popular open-access CXR datasets, taxonomy of the state-of-the-art works to assist the researchers in preparing the plan for their research contribution, and discuss potential future research directions in this field. More than 350 research papers from various indexing services, including Web of Science, Scopus, PubMed, and IEEE, were considered for this review article. After several selection parameters, it is observed that most of the literature focuses on the DL approach with CXRs. However, few publications have focused on CT scans and Ultrasound images.

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“…Among the various network architectures used in deep learning techniques, the U-Net has shown its significance for and great performance in medical image segmentation [30][31][32]. In addition, accumulating studies using the U-Net model have promising results in lung segmentation in CXR images [33,34] and further studies have already been conducted in TB-specific images [12,21,35]. However, when reviewing these published studies, which use various datasets [29,[36][37][38], the precise size and exact location of TB lesions annotated using a closed contour are not provided, and all of these studies lack object-level annotations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images

Ou,

Chen,

Chang

et al. 2024

Diagnostics

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We present a deep learning (DL) network-based approach for detecting and semantically segmenting two specific types of tuberculosis (TB) lesions in chest X-ray (CXR) images. In the proposed method, we use a basic U-Net model and its enhanced versions to detect, classify, and segment TB lesions in CXR images. The model architectures used in this study are U-Net, Attention U-Net, U-Net++, Attention U-Net++, and pyramid spatial pooling (PSP) Attention U-Net++, which are optimized and compared based on the test results of each model to find the best parameters. Finally, we use four ensemble approaches which combine the top five models to further improve lesion classification and segmentation results. In the training stage, we use data augmentation and preprocessing methods to increase the number and strength of lesion features in CXR images, respectively. Our dataset consists of 110 training, 14 validation, and 98 test images. The experimental results show that the proposed ensemble model achieves a maximum mean intersection-over-union (MIoU) of 0.70, a mean precision rate of 0.88, a mean recall rate of 0.75, a mean F1-score of 0.81, and an accuracy of 1.0, which are all better than those of only using a single-network model. The proposed method can be used by clinicians as a diagnostic tool assisting in the examination of TB lesions in CXR images.

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Segmentation of Chest X-Ray Images Using U-Net Model

Cited by 7 publications

References 19 publications

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

A systematic survey on deep learning techniques for chest disease detection using chest radiographs

Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images

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