A deep learning method for classification of chest X-ray images

Zhao, Jiashi; Li, Mengmeng; Shi, Weili; Yu, Ming; Jiang, Zhengang; Ji, Bing

doi:10.1088/1742-6596/1848/1/012030

Cited by 13 publications

(5 citation statements)

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“…To identify 14 chest diseases from CXR images, an AMDenseNet model with an attention mechanism was suggested. Their DenseNet-121-based models outperformed prior research in terms of average AUC [77,78].…”

Section: Multiple Disease Detectionmentioning

confidence: 89%

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Arslan,

Haider,

Khurshid

et al. 2023

Cureus

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Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

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Section: Multiple Disease Detectionmentioning

confidence: 89%

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Arslan,

Haider,

Khurshid

et al. 2023

Cureus

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“…It achieved an average AUC achieved of 79.50%. Zhao et al [150] proposed a DCNN model with attention mechanism (AMDenseNet) to predict the presence of 14 chest diseases using CXR images from the Chest-Xray14 dataset. The model based on DenseNet-121 achieved a high average AUC of 85.37% outperforming the state-of-the-art works, such as [39,149].…”

Section: Multiple Disease 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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“…Several high-performing models utilize transfer learning by pretraining CNNs on the ImageNet dataset and fine-tuning them on the target dataset [ 45 ]. Notable architectures used for this purpose include ResNets [ 46 , 47 ], DenseNets [ 20 , 48 , 49 ], Swin Transformers [ 29 ], and ConvNeXts [ 37 , 50 ]. Irvin et al [ 20 ] use the Densenet121 architecture to achieve an AUC of 0.90 on the multi-class classification problem.…”

Section: Introductionmentioning

confidence: 99%

“…Many works incorporate attention mechanisms into CNNs to focus on regions of interest and improve diagnostic accuracy [ 32 , 47 , 49 , 53 ]. Many studies use ensembles of machine learning classifiers to improve disease classification using CXR images [ 29 , 40 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies use ensembles of machine learning classifiers to improve disease classification using CXR images [ 29 , 40 , 43 ]. Zhao et al [ 49 ] achieves an AUC of 0.85 on a 14-class chest X-ray classification task, using an attention module added to the DensNet121 architecture and the focal loss for combating class imbalance. Various image processing and augmentation techniques have been used to improve performance and generalization of deep learning models [ 36 , 54 , 55 ].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

et al. 2023

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Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or ‘equivocal’ images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the ‘equivocal’ class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.

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A deep learning method for classification of chest X-ray images

Cited by 13 publications

References 1 publication

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

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

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

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