Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
Source abstract
Abstract
Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models, which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database. These models are trained on the chest X-ray data set using fine-tuning. Finally, the results are obtained by combining the extracted features from these three models during the experimental phase. The proposed EL approach outperforms other existing state-of-the-art methods and obtains an accuracy of 93.91% and a F1-score of 93.88% on the testing phase.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Phonocardiography and Auscultation Techniques · Digital Imaging for Blood Diseases
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, Egypt
Information and Computing Lab, atlanTTic Research Center, Telecommunication Engineering School, Universidade de Vigo, 36310 Vigo, Spain
Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria
Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef 62511, Egypt
Funding
the European Regional Development Fund (ERDF) · the Galician Regional Government · Spanish Government
References (54)
Show source-provided references
1. Automatic pneumonia detection in chest X-ray images using textural features10.1016/j.compbiomed.2022.105466 ↗
2. Fusion of convolutional neural networks based on Dempster–Shafer theory for automatic pneumonia detection from chest X-ray images10.1002/ima.22653 ↗
3. Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 202010.1016/j.ejrad.2021.110069 ↗
4. Study of deep learning techniques for medical image analysis: A review10.1016/j.matpr.2022.01.071 ↗
5. Iori, M., Di Castelnuovo, C., Verzellesi, L., Meglioli, G., Lippolis, D.G., Nitrosi, A., Monelli, F., Besutti, G., Trojani, V., and Bertolini, M. (2022). Mortality prediction of COVID-19 patients using radiomic and neural network features extracted from a wide chest X-ray sample size: A robust approach for different medical imbalanced scenarios. Appl. Sci., 12.10.3390/app12083903 ↗
6. Transparency of deep neural networks for medical image analysis: A review of interpretability methods10.1016/j.compbiomed.2021.105111 ↗
7. Kale, S.P., Patil, J., Kshirsagar, A., and Bendre, V. (2022). Early Lungs Tuberculosis Detection Using Deep Learning. Intelligent Sustainable Systems, Springer.10.1007/978-981-16-6309-3_29 ↗
8. Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts10.1016/j.polymertesting.2022.107540 ↗
9. Large-scale recognition of natural landmarks with deep learning based on biomimetic sonar echoes
10. Le Dinh, T., Lee, S.H., Kwon, S.G., and Kwon, K.R. (2022). COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks. Appl. Sci., 12.10.3390/app12104861 ↗
11. Sajun, A.R., Zualkernan, I., and Sankalpa, D. (2022). Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays. Appl. Sci., 12.10.3390/app12094694 ↗
12. Furtado, A., Andrade, L., Frias, D., Maia, T., Badaró, R., and Nascimento, E.G.S. (2022). Deep Learning Applied to Chest Radiograph Classification—A COVID-19 Pneumonia Experience. Appl. Sci., 12.10.3390/app12083712 ↗
13. Malhotra, P., Gupta, S., Koundal, D., Zaguia, A., Kaur, M., and Lee, H.N. (2022). Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images. Sensors, 22.10.3390/s22062278 ↗
14. Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things
15. Adel, H., Dahou, A., Mabrouk, A., Abd Elaziz, M., Kayed, M., El-Henawy, I.M., Alshathri, S., and Amin Ali, A. (2022). Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm. Mathematics, 10.10.3390/math10030447 ↗
16. Distant domain transfer learning for medical imaging10.1109/JBHI.2021.3051470 ↗
17. Lee, H.C., and Aqil, A.F. (2022). Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification. Appl. Sci., 12.10.3390/app12031040 ↗
18. Mabrouk, A., Redondo, R.P.D., and Kayed, M. (2021). SEOpinion: Summarization and Exploration of Opinion from E-Commerce Websites. Sensors, 21.10.3390/s21020636 ↗
19. Chandrasekaran, G., Antoanela, N., Andrei, G., Monica, C., and Hemanth, J. (2022). Visual Sentiment Analysis Using Deep Learning Models with Social Media Data. Appl. Sci., 12.10.3390/app12031030 ↗
20. Deep learning-based sentiment classification: A comparative survey10.1109/ACCESS.2020.2992013 ↗
21. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv.
22. Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., and Keutzer, K. (2014). Densenet: Implementing efficient convnet descriptor pyramids. arXiv.
23. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 × 16 words: Transformers for image recognition at scale. arXiv.
24. Hierarchical convolutional models for automatic pneu-monia diagnosis based on X-ray images: New strategies in public health
25. Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., and Xu, Y. (2022). A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell.10.1109/TPAMI.2022.3152247 ↗
26. Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-ray Images10.1007/s13369-021-06127-z ↗
27. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv.
28. An efficient deep learning approach to pneumonia classification in healthcare10.1155/2019/4180949 ↗
29. Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks10.3233/JIFS-191438 ↗
30. Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis10.1016/j.media.2019.03.009 ↗
31. Ayan, E., and Ünver, H.M. (2019, January 24–26). Diagnosis of pneumonia from chest X-ray images using deep learning. Proceedings of the 2019 IEEE Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey.10.1109/EBBT.2019.8741582 ↗
32. Chouhan, V., Singh, S.K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., and De Albuquerque, V.H.C. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl. Sci., 10.10.3390/app10020559 ↗
33. Rahman, T., Chowdhury, M.E., Khandakar, A., Islam, K.R., Islam, K.F., Mahbub, Z.B., Kadir, M.A., and Kashem, S. (2020). Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Appl. Sci., 10.10.3390/app10093233 ↗
34. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models10.1016/j.irbm.2019.10.006 ↗
35. Mittal, A., Kumar, D., Mittal, M., Saba, T., Abunadi, I., Rehman, A., and Roy, S. (2020). Detecting pneumonia using convolutions and dynamic capsule routing for chest X-ray images. Sensors, 20.10.3390/s20041068 ↗
36. A transfer learning method with deep residual network for pediatric pneumonia diagnosis10.1016/j.cmpb.2019.06.023 ↗
37. Chen, Y., Fan, H., Xu, B., Yan, Z., Kalantidis, Y., Rohrbach, M., Yan, S., and Feng, J. (2019, January 27–28). Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea.10.1109/ICCV.2019.00353 ↗
38. Li, P., Yang, Y., Grosu, R., Wang, G., Li, R., Wu, Y., and Huang, Z. (2021). Driver Distraction Detection Using Octave-Like Convolutional Neural Network. IEEE Trans. Intell. Transp. Syst.10.1109/TITS.2021.3086411 ↗
39. das Neves, R.B., Verçosa, L.F., Macêdo, D., Bezerra, B.L.D., and Zanchettin, C. (2020, January 18–23). A fast fully octave convolutional neural network for document image segmentation. Proceedings of the 2020 IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.10.1109/IJCNN48605.2020.9206711 ↗
40. Accurate tumor segmentation via octave convolution neural network
41. Mahmoudi, R., Benameur, N., Mabrouk, R., Mohammed, M.A., Garcia-Zapirain, B., and Bedoui, M.H. (2022). A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging. Appl. Sci., 12.10.3390/app12104825 ↗
42. Chen, P.Y., Zhang, X.H., Wu, J.X., Pai, C.C., Hsu, J.C., Lin, C.H., and Pai, N.S. (2022). Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network. Appl. Sci., 12.10.3390/app12084079 ↗
43. Identifying medical diagnoses and treatable diseases by image-based deep learning10.1016/j.cell.2018.02.010 ↗
44. Rajaraman, S., Candemir, S., Kim, I., Thoma, G., and Antani, S. (2018). Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl. Sci., 8.10.3390/app8101715 ↗
45. Structural crack detection using deep convolutional neural networks10.1016/j.autcon.2021.103989 ↗
46. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
47. An approach for multiclass skin lesion classification based on ensemble learning10.1016/j.imu.2021.100659 ↗
48. Gupta, K.D., Sharma, D.K., Ahmed, S., Gupta, H., Gupta, D., and Hsu, C.H. (2021). A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT. Neural Process. Lett., 1–24.
49. Kassani, S.H., Kassani, P.H., Wesolowski, M.J., Schneider, K.A., and Deters, R. (2019). Classification of histopathological biopsy images using ensemble of deep learning networks. arXiv.
50. Li, Y., Zhang, K., Cao, J., Timofte, R., and Van Gool, L. (2021). Localvit: Bringing locality to vision transformers. arXiv.
51. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
52. Pérez, E., and Ventura, S. (2021). An ensemble-based convolutional neural network model powered by a genetic algorithm for melanoma diagnosis. Neural Comput. Appl., 1–20.10.1007/s00521-021-06655-7 ↗
53. Madani, A., Moradi, M., Karargyris, A., and Syeda-Mahmood, T. (2018, January 11–13). Chest x-ray generation and data augmentation for cardiovascular abnormality classification. Proceedings of the Medical Imaging 2018: Image Processing, Houston, TX, USA.10.1117/12.2293971 ↗
54. Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images10.1259/bjr.20201263 ↗