Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database

Sirazitdinov, Ilyas; Kholiavchenko, Maksym; Mustafaev, Tamerlan; Yuan, Yixuan; Kuleev, Ramil; Ibragimov, Bulat

doi:10.1016/j.compeleceng.2019.08.004

Cited by 172 publications

(106 citation statements)

References 8 publications

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“…Rajaraman et al [39] tried to explain the performance of customized CNNs to detect pneumonia and further differentiate between bacterial and viral types in pediatric CXRs. Sirazitdinov et al [40] used a region based convolutional neural network for segmenting the pulmonary images along with image augmentation for pneumonia identification. Lakhani and Sundaram [41] used the AlexNet and GoogLeNet neural networks with data augmentation and without any pre-training to obtain an area under the curve (AUC) of 0.94-0.95.…”

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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“…RetinaNet and Mask R-CNN models were also used in [18]. Since the FPN produces multi-scale feature maps with greater quality information than the default, the FPN base was used as the backbone of both models.…”

Section: Related Workmentioning

confidence: 99%

“…Other researchers have used performance metrics, as in [12], where only Area Under the Curve (AUC) was used, in [13], where only F1-score was used, and in [19], where only the accuracy metric was used. Moreover, in [15][16][17][18], accuracy and other metrics were used. However, the author in [1] is the only one that has used all performance metrics, as in our model.…”

Section: Related Workmentioning

confidence: 99%

Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

2020

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Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world. Several deep learning models for detecting pneumonia from chest X-ray images have been proposed. One of the extreme challenges has been to find an appropriate and efficient model that meets all performance metrics. Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work. In this paper, four different models are developed by changing the used deep learning method; two pre-trained models, ResNet152V2 and MobileNetV2, a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM). The proposed models are implemented and evaluated using Python and compared with recent similar research. The results demonstrate that our proposed deep learning framework improves accuracy, precision, F1-score, recall, and Area Under the Curve (AUC) by 99.22%, 99.43%, 99.44%, 99.44%, and 99.77%, respectively. As clearly illustrated from the results, the ResNet152V2 model outperforms other recently proposed works. Moreover, the other proposed models—MobileNetV2, CNN, and LSTM-CNN—achieved results with more than 91% in accuracy, recall, F1-score, precision, and AUC, and exceed the recently introduced models in the literature.

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“…Additionally, on Kermany's dataset, multiple works had been presented; the three latest top results are by Lian and Zheng [28], Luján et al [30], and Chouhan et al [31]. On the RSNA Challenge [32], the top result was presented by Sirazitdinov et al [33].…”

Section: Convolutional Neural Network Pneumonia and Covid-19mentioning

confidence: 99%

Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

et al. 2020

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As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia.

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Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database

Cited by 172 publications

References 8 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

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