A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

By V. Chouhan, S. Singh, A. Khamparia, Deepak Gupta, Prayag Tiwari, Catarina Moreira, V. H. C. de Albuquerque. Applied Sciences, 2020

633 citations recorded by the source corpus.

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/119baf88111d8015dfbd38c79aaa3f7348d7e042

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Hybrid deep learning for detecting lung diseases from X-ray images (2020, neutral): “[28] where several transfer learning methods such as DenseNet121, AlexNet, Inception V3, etc.”
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray (2020, methodology): “[28] for the detection of pneumonia using pre-trained ImageNet models [31] and their ensembles.”
New machine learning method for image-based diagnosis of COVID-19 (2020, background): “ML has demonstrated high performance for several image processing applications such as image analysis [5, 6], image classification [7], and image segmentation [8].”
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.”
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): “[12] detected the images of pneumonia using deep learning models, three classes of the dataset: normal, virus pneumonia, and bacterial pneumonia images.”
Deep learning approaches for COVID-19 detection based on chest X-ray images (2020, positive): “Chouhan et al. (2020) detected pneumonia in chest X-ray images using five new deep-transfer-learning-based models applied as an ensemble.”
Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning (2020, positive): “Chouhan et al. [48] introduced an ensemble deep model that combines outputs from all transfer deep models for the classiﬁcation of pneumonia using the connotation of deep learning.”