Abstract

Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.

Keyword

Pneumonia, Machine learning, Deep learning, Convolution neural networks, Data augmentation.

Cite this article

AlSumairi SB, Ben Ismail MM

Refference

[1][1]Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. Automatic detection of acute bacterial pneumonia from chest X-ray reports. Journal of the American Medical Informatics Association. 2000; 7(6):593-604.

[2][2]Förster A, Brandstetter S, Schulze-Briese C. Transforming X-ray detection with hybrid photon counting detectors. Philosophical Transactions of the Royal Society A. 2019.

[3][3]Sousa RT, Marques O, Soares FA, Sene Jr II, de Oliveira LL, Spoto ES. Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs. Procedia Computer Science. 2013; 18:2579-82.

[4][4]Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225. 2017.

[5][5]Stephen O, Sain M, Maduh UJ, Jeong DU. An efficient deep learning approach to pneumonia classification in healthcare. Journal of Healthcare Engineering. 2019.

[6][6]Ayan E, Ünver HM. Diagnosis of pneumonia from chest X-ray images using deep learning. In scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT) 2019 (pp. 1-5). IEEE.

[7][7]Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In proceedings of the conference on computer vision and pattern recognition 2017 (pp. 4700-8).

[8][8]Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. In conference on computer vision and pattern recognition 2009 (pp. 248-55). IEEE.

[9][9]Sirazitdinov I, Kholiavchenko M, Mustafaev T, Yixuan Y, Kuleev R, Ibragimov B. Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Computers & Electrical Engineering. 2019; 78:388-99.

[10][10]Hoang TM, Nguyen PH, Truong NQ, Lee YW, Park KR. Deep retinanet-based detection and classification of road markings by visible light camera sensors. Sensors. 2019.

[11][11]He K, Gkioxari G, Dollár P, Girshick R. Mask r-cnn. In proceedings of the IEEE international conference on computer vision 2017 (pp. 2961-9).

[12][12]Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A. Comparison of deep learning approaches for multi-label chest X-ray classification. Scientific Reports. 2019; 9(1):1-10.

[13][13]Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017; 284(2):574-82.

[14][14]Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In advances in neural information processing systems 2012 (pp. 1097-105).

[15][15]Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In proceedings of the IEEE conference on computer vision and pattern recognition 2015 (pp. 1-9).

[16][16]Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ. Identifying pneumonia in chest X-rays: a deep learning approach. Measurement. 2019; 145:511-8.

[17][17]He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In proceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770-8).

[18][18]https://www.kaggle.com/pytorch/resnet101. Accessed 26 June 2020.

[19][19]Varshni D, Thakral K, Agarwal L, Nijhawan R, Mittal A. Pneumonia detection using CNN based feature extraction. In international conference on electrical, computer and communication technologies 2019 (pp. 1-7). IEEE.

[20][20]Chollet F. Xception: Deep learning with depthwise separable convolutions. In proceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 1251-8).

[21][21]Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014.

[22][22]Sarkar R, Hazra A, Sadhu K, Ghosh P. A novel method for pneumonia diagnosis from chest x-ray images using deep residual learning with separable convolutional networks. In computer vision and machine intelligence in medical image analysis 2020 (pp. 1-12). Springer, Singapore.

[23][23]Rajaraman S, Candemir S, Kim I, Thoma G, Antani S. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences. 2018.

[24][24]Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Medicine. 2018.

[25][25]Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine. 2018.

[26][26]Abiyev RH, Ma’aitah MK. Deep convolutional neural networks for chest diseases detection. Journal of Healthcare Engineering. 2018.

[27][27]Cilimkovic M. Neural networks and back propagation algorithm. Institute of Technology Blanchardstown, Blanchardstown Road North Dublin. 2015.

[28][28]Abas AR. Adaptive competitive learning neural networks. Egyptian Informatics Journal. 2013; 14(3):183-94.

[29][29]Toğaçar M, Ergen B, Cömert Z, Özyurt F. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. IRBM. 2020; 41(4):212-22.

[30][30]Behzadi-khormouji H, Rostami H, Salehi S, Derakhshande-Rishehri T, Masoumi M, Salemi S, et al. Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. Computer Methods and Programs in Biomedicine. 2020; 185:105162.

[31][31]Ullah I, Petrosino A. About pyramid structure in convolutional neural networks. In international joint conference on neural networks 2016 (pp. 1318-24). IEEE.

[32][32]Antin B, Kravitz J, Martayan E. Detecting pneumonia in chest x-rays with supervised learning. http://cs229.stanford.edu/proj2017/final-reports/5231221.pdf. Accessed 26 June 2020.

[33][33]Karthick G, Harikumar R. Comparative performance analysis of naive bayes and SVM classifier for oral X-ray images. In international conference on electronics and communication systems 2017 (pp. 88-92). IEEE.

[34][34]Oliveira LL, e Silva SA, Ribeiro LH, de Oliveira RM, Coelho CJ, Andrade AL. Computer-aided diagnosis in chest radiography for detection of childhood pneumonia. International Journal of Medical Informatics. 2008; 77(8):555-64.

[35][35]https://www.kaggle.com/c/rsna-pneumonia-detection-challenge. Accessed 26 June 2020.