Automatic detection of pleural effusion in chest radiographs

Maduskar, Pragnya; Philipsen, Rick H. H. M.; Melendez, Jaime; Scholten, Ernst Th.; Chanda, Duncan; Ayles, Helen; Sánchez, Clara I.; Ginneken, Bram van

doi:10.1016/j.media.2015.09.004

Cited by 37 publications

(18 citation statements)

References 32 publications

(37 reference statements)

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“…1 d ) helps in detection of pleural effusion that can be due to pneumonia, cirrhosis, pulmonary embolism, and tuberculosis (TB). Various CADx systems [16, 38, 39] use CP angle to detect pleural effusion in CXRs. E. Expanded lung : CXRs are generally acquired after the patient inhales and holds the breath.…”

Section: Chest Anatomymentioning

confidence: 99%

Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning

Mittal

Hooda

Sofat

2017

IET Image Processing

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Section: Chest Anatomymentioning

confidence: 99%

Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning

Mittal

Hooda

Sofat

2017

IET Image Processing

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“…Xu et al [ 14 ] also researched the detection of cavities. Maduskar et al [ 15 ] proposed a method to locate ribs using the chest wall contour as the landmark to automatically detect pleural effusion. These systems use only one type of tuberculosis lesion for target detection, and thus they cannot achieve comprehensive tuberculosis screening.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

Xie

Han

et al. 2020

Journal of Healthcare Engineering

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The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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“…lung disease often occurs in developing countries along with human immunodeficiency virus (HIV) and diabetes, which will immediately affect the immunity and infection of lung disease. This disease is a respiratory disease, meaning that it causes lung infections in the thoracic area of patients [1,2]. In early diagnosis by a doctor, chest X-ray (CRX) films are used.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

2020

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Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.

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Automatic detection of pleural effusion in chest radiographs

Cited by 37 publications

References 32 publications

Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning

Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

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