Feature Extraction and Classification of Chest X-Ray Images Using CNN to Detect Pneumonia

Sharma, Harsh; Jain, Jai; Bansal, Poonam; Gupta, Sumit

doi:10.1109/confluence47617.2020.9057809

Cited by 146 publications

(75 citation statements)

References 12 publications

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“…In their experiments, the training images were divided into two parts: actual training, 90%, and validation, 10%. The final model and the RetinaNet and In [19], the authors presented two CNN architectures-one with a dropout layer and another without a dropout layer. Both CNNs consisted of a convolution layer, a maximum pooling layer, and a classification layer.…”

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.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 19 ], the authors presented two CNN architectures—one with a dropout layer and another without a dropout layer. Both CNNs consisted of a convolution layer, a maximum pooling layer, and a classification layer.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

2020

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“…Different procedures are present in literature that can help to recognize pneumonia by working on chest radiography images. In another study different deep CNN models were proposed to extract features from images of chest radiography [16].…”

Section: Related Workmentioning

confidence: 99%

Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)

Walia¹,

Srivastava²,

Kumar³

et al. 2020

EAI Endorsed Trans Perv Health Tech

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Pneumonia is most significant disease in today's world. It resulted around 15 % of the total deaths of children of the same age group. OBJECTIVES: This paper proposes Depth Wise Convolution Neural Network (DW-CNN) using the SWISH Activation and Transfer Learning (VGG16) to reliably diagnose pneumonia. METHODS: The proposed model contains 10 layers of convolutional neural networks. Also, three dense layers with the Swish activation function with a dropout of 0.7 and 0.5 respectively in each layer. The model was trained on 5216 augmented with weighted contrast and brightened radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16). RESULT: The model was trained on 5216 augmented radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16) and the final results obtained with training accuracy of 98.5%, testing accuracy of 79.8% and validation accuracy of 75%. CONCLUSION: The model can be improved by using different transfer learning models and hyperparameter tuning parameters.

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“…There are several databases of CXR images. The studies below will be discussed in two categories: studies [5][6][7][8][9][10] using other databases, and studies [2,[11][12][13][14][15][16][17][18][19] using the same database [20] with this study. All of these studies have classified CXR images into two broad categories: normal and pneumonia.…”

Section: Introductionmentioning

confidence: 99%

Determination of Pneumonia in X-ray Chest Images by Using Convolutional Neural Network

Polat¹,

Dokur²,

Ölmez³

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Pneumonia is one of the major diseases that causes a lot of deaths all over the world. Determining pneumonia from chest x-ray (CXR) images is an extremely difficult and important image processing problem. The discrimination of whether pneumonia is of bacterium or virus origin has also become more important during the pandemic. Automatic determination of the presence and origin of pneumonia is crucial for speeding up the treatment process and increasing the patient's survival rate. In this study, a convolutional neural network (CNN) framework is proposed for detection of pneumonia from CXR images. Two different binary CNNs and a triple CNN are used for determining: (i) normal or pneumonia, (ii) pneumonia of bacterium or virus origin, and (iii) normal or bacterial pneumonia or viral pneumonia. In this approach, CNNs are trained with Walsh functions to extract the features from CXR images, and minimum distance classifier (MDC) instead of a fully connected neural network (FCNN) is employed for classification purpose. Training with Walsh functions maintains the within-class scattering to be low, and between-class scattering to be high. Preferring the minimum distance classifier reduces the number of nodes used and also allows the network to be controlled with fewer hyper-parameters. These approaches bring some advantages to the system designed for the classification process: (i) easily determination of hyper-parameters, (ii) achieving higher classification performance, and (iii) use of fewer neurons. Proposed small-size CNN model was applied to CXR images from one to five years old children provided by the Guangzhou Women's and Children's Medical Center (GWCMC). Three experiments have been conducted to improve the classification performance: (i) the effect of different sizes of input images on the performance of the network was investigated, (ii) training set was augmented by randomly flipping left to right or down to up, by adding Gaussian noise to the images, by creating negative images randomly, and by changing image brightness randomly (iii) instead of RGB CXR images, gray component of the original image and four 2D wavelet images were given as input to the network. In these experiments, no major changes were observed in the classification results obtained by using the proposed CNNs. Proposed method has achieved 100% accuracy for normal or pneumonia, 92% for pneumonia of bacterium or virus origin, and 90% for normal or bacterial pneumonia or viral pneumonia. It is observed that higher classification performances were obtained with approximately five times less parameters compared to the networks that gave the best results in the literature. Thus, the applied CNN model is promising in medicine and can help experts make quick and accurate decisions.

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Feature Extraction and Classification of Chest X-Ray Images Using CNN to Detect Pneumonia

Cited by 146 publications

References 12 publications

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

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

Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)

Determination of Pneumonia in X-ray Chest Images by Using Convolutional Neural Network

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