Identifying pneumonia in chest X-rays: A deep learning approach

By Amit Kumar Jaiswal, Prayag Tiwari, Sachin Kumar, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues. Measurement, 2019

434 citations recorded by the source corpus.

Abstract The rich collection of annotated datasets piloted the robustness of deep learning techniques to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths include children under age five are caused by pneumonia globally. In this study, we describe our deep learning based approach for the identification and localization of pneumonia in Chest X-rays (CXRs) images. Researchers usually employ CXRs for the diagnostic imaging study. Several factors such as positioning of the patient and depth of inspiration can change the appearance of the chest X-ray, complicating interpretation further. Our identification model ( https://github.com/amitkumarj441/identify_pneumonia ) is based on Mask-RCNN, a deep neural network which incorporates global and local features for pixel-wise segmentation. Our approach achieves robustness through critical modifications of the training process and a novel post-processing step which merges bounding boxes from multiple models. The proposed identification model achieves better performances evaluated on chest radiograph dataset which depict potential pneumonia causes.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0715fbce77ea4d2dcb6ab6d5606cdd00bdb3e776

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A review on deep learning in medical image analysis (2021, methodology): “A density neural network is Kernel Density Estimation, which uses small groups of closely related data samples to estimate the distribution of new points in the problem space [34–38].”
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches (2020, neutral): “Many deep learning studies have detected the disease using a chest X-ray image data approach [7].”
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray (2020, background): “Several research groups [1, 12-25] have reported the use of deep machine learning algorithms in the detection of pneumonia, however only an article [16] has reported the classification of bacterial and viral pneumonia.”
Pneumonia Detection On Chest X-Ray Using Radiomic Features And Contrastive Learning (2021, background): “With the great success of deep learning in various fields, deep neural networks (DNNs) have proven to be powerful tools that can detect pneumonia to augment radiologists [3, 4, 5, 6].”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, neutral): “Diagnosis of cardiac arrhythmia [13,14] , brain injuries [15,16] , lung segmentation [17,18] , breast cancer [19,20] , skin cancer [21,22] , epilepsy [23,40,72] , and pneumonia [24-29] with deep learning models has increased the popularity of these algorithms in biomedical ﬁeld.”
Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning (2022, background): “Human organ disorders are typically assessed using a variety of imaging technologies, including magnetic resonance imaging (MRI) [5], positron emission tomography (PET) [6], and X-rays [7].”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “Chest radiography (X-ray) is one of the most important methods used for the diagnosis of pneumonia worldwide [7].”