Pneumonia Identification in Chest X-Ray Images Using EMD

Khatri, Archit; Jain, Rishabh; Vashista, Hariom; Mittal, Nityam; Ranjan, Priya; Janardhanan, Rajiv

doi:10.1007/978-981-15-1624-5_9

Cited by 52 publications

(25 citation statements)

References 18 publications

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“…Several authors also have worked on pneumonia classification. Khatri et al [35] proposed the use of EMD (earth mover's distance) to identify infected pneumonia lungs from normal non-infected lungs. Rahib et al [36] and Okeke et al [37] used a CNN model for pneumonia classification.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

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Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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“…Recently several researchers have worked on classification of pneumonia. Khatri et al [34] suggested to utilize earth movers distance (EMD) algorithm to classify non-infected and infected lungs. Preprocessing is performed on the source image to remove all non-lung areas.…”

Section: Related Workmentioning

confidence: 99%

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

et al. 2022

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In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to diagnose the virus from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

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“…Khatri et al [91] proposed EarthMovers Distance (EMD) for pneumonia detection from CXR images. The authors preprocessed the images by cropping the lung regions, normalized the intensity, and calculated the EMD difference to distinguish the pneumonia samples from non-pneumonia samples.…”

Section: A Traditional Machine Learningmentioning

confidence: 99%

“…The proposed method accurately visualizes the location of the disease in the x-ray image, improving the disease interpretation. First, the image input is processed by Preact-Resnet (Resnet-v2) [91], which extracts the feature tensors of size ℎ' = ℎ/32, ' = /32 and ' = 2048. Here, ℎ, , and are the height, width, and number of channels of the input image, respectively.…”

Section: ) Segmentation By Fcn and Rcnnmentioning

confidence: 99%

Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review

2021

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Chest radiography is a significant diagnostic tool used to detect diseases afflicting the chest. The automatic detection techniques associated with computer vision are being adopted in medical imaging research. Over the last decade, several remarkable advancements have been made in the field of medical diagnostics with the application of deep learning techniques. Various automated systems have been proposed for the rapid detection of pneumonia from chest X-rays. Although several algorithms are currently available for pneumonia detection, a detailed review summarizing the literature and offering guidelines for medical practitioners is lacking. This study will help practitioners to select the most effective and efficient methods from a real-time perspective, review the available datasets, and understand the results obtained in this domain. It will also present an overview of the literature on intelligent pneumonia identification from chest X-rays. The usability, goodness factors, and computational complexities of the algorithms employed for intelligent pneumonia identification are analyzed. Additionally, this study discusses the quality, usability, and size of the available chest X-ray datasets and techniques for coping with unbalanced datasets. A detailed comparison of the available studies reveals that the majority of the applied datasets are highly unbalanced and limited, providing unreliable results and rendering methods that are unsuitable for large-scale use. Large-scale balanced datasets can be obtained via smart techniques, such as generative adversarial networks. Current literature has indicated that deep learning-based algorithms achieve the best results for pneumonia classification with an accuracy of 98.7%, a sensitivity of 0.99, and a specificity of 0.98. The higher accuracy offered by deep-learning algorithms in addition to their appropriate class balancing techniques serves as a good reference for further research.

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Pneumonia Identification in Chest X-Ray Images Using EMD

Cited by 52 publications

References 18 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review

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