Identification of Pneumonia in Chest X-Ray Image Based on Transformer

Yong-jun, MA; Lv, Wei

doi:10.1155/2022/5072666

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…Introduced in 2015 as part of the ResNet family of models, its popularity can be attributed to its ability to handle complex data sets. Moreover, ResNet-50 has also been used as a standard CNN model for comparison in different computer vision research, ,− making it an ideal candidate for evaluating the generalizability of the GAN-based data augmentation approach in determining whether it can improve model performance across a broader range of scenarios.…”

Section: Resultsmentioning

confidence: 99%

“…The advantage of Swin-Transformer is its significantly faster processing efficiency compared to the conventional vision transformer . Furthermore, Swin-Transformer has achieved state-of-the-art results in various fields, including the detection of parasitic eggs and pneumonia in chest X-ray images …”

Section: Methodsmentioning

confidence: 99%

“…30 Furthermore, Swin-Transformer has achieved state-of-the-art results in various fields, 26 including the detection of parasitic eggs 31 and pneumonia in chest X-ray images. 32 All models in this work were built with the assistance of MMDetection 33 from OpenMMLab machine-learning frameworks. To train the models, the backbone models' weights were initialized using pretrained weights from ImageNet-1K.…”

Section: Training Of Cascade Mask R-cnn Model For Algae Detectionmentioning

confidence: 99%

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Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

Fung,

Chan,

et al. 2023

ACS EST Water

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Identifying and quantifying algal genera in images are crucial for understanding their ecological impact. Algal data are often imbalanced, limiting detection model accuracy. This paper presents a novel data augmentation method using StyleGAN2-ADA to enhance algal image instance segmentation. StyleGAN2-ADA generates artificial single-algal images to address data scarcity and imbalance. We train a Cascaded Mask R-CNN with Swin Transformer on a combined data set of real and artificial multigenera algal images and evaluate performance using the COCO mAP metric. The approach improves bounding box detection performance by 17.9% on all genera and 32.1% on rare genera compared with the baseline model. Additionally, 50% more artificial data yield significant enhancements without excessive artificial data use. The GAN-based augmentation technique shows a performance improvement in both Swin-Tiny and ResNet-50 backbone models, suggesting adaptability for various machine learning models. The increased mAP leads to the accurate identification of harmful algae genera, allowing for better prevention and mitigation. This method offers a superior data augmentation solution for accurate algal instance segmentation and can benefit applications challenged by imbalanced and scarce data.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Training Of Cascade Mask R-cnn Model For Algae Detectionmentioning

confidence: 99%

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Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

Fung,

Chan,

et al. 2023

ACS EST Water

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“…Multiple works were developed in the context to avoid misdiagnosing pneumonia, as shown in Table 7. For example, Ma and Lv [99] proposed a Swin transformer model for features extraction with a fully connected layer for classification of pneumonia in CXR images. The performance of the model was evaluated against DCNN models using two different datasets (Pediatric-CXR and ChestX-ray8).…”

Section: Pneumonia Detectionmentioning

confidence: 99%

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Nasser

Akhloufi

2023

Diagnostics

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Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models’ detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases.

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“…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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Identification of Pneumonia in Chest X-Ray Image Based on Transformer

Cited by 14 publications

References 18 publications

Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

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

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