A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models

Toğaçar, Mesut; Ergen, Burhan; Cömert, Zafer; Özyurt, Fatih

doi:10.1016/j.irbm.2019.10.006

Cited by 233 publications

(119 citation statements)

References 36 publications

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“…Rajaraman et al [39] used customized CNNs to detect pneumonia and reported a test accuracy of 96.2%. M.Togacar et al [60] combined features from different deep learning models for pneumonia classification and achieved an accuracy of 96.84%. Vikash et al [51] combined the outputs of different neural networks and reached the final prediction using majority voting.…”

Section: Comparative Analysis Of Various Existing Methodsmentioning

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: Comparative Analysis Of Various Existing Methodsmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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“…The authors in [35] used CNN architectures, including InceptionV3, InceptionResnetV2, and Xception for classification of Chest X-ray images while statistical algorithms, like, Markov chain Monte Carlo (MCMC) and genetic algorithms are used to tune the hyperparameters of the models. In [36] the authors used pre-trained CNN models such as AlexNet, Vgg16, Vgg19 for feature extraction. The features obtained from the models mentioned above were then reduced with the help of minimum redundancy maximum relevance algorithm.…”

Section: Related Workmentioning

confidence: 99%

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Gupta

Anjum

Gupta

et al. 2021

Applied Soft Computing

197

121

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Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

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“…In 2019, the authors in [15] published a work concerning pneumonia detection applying a combination of mRMR feature selection and machine learning models on the Chest X-ray images. Features extraction is performed using existing CNN models: AlexNet, VGG16 and VGG19 and then combining the results as an input features set to feed and train five different machine learning models: decision tree, k-nearest neighbors, linear discriminant analysis, linear regression, and support vector.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

Moujahid¹,

Cherradi²,

Gannour³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image's classification tasks. Through this article, we propose an implementation a CNNbased classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.

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A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models

Cited by 233 publications

References 36 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

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