An Efficient Hypertuned DNN Based Approach for Pneumonia Detection

Sayali Abhijeet Salkade  lowast; Sheetal Rathi

doi:10.17485/IJST/v17i25.1230

Article

An Efficient Hypertuned DNN Based Approach for Pneumonia Detection

VIEWS 45
PDF 33

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i25.1230

Year: 2024, Volume: 17, Issue: 25, Pages: 2667-2678

Original Article

An Efficient Hypertuned DNN Based Approach for Pneumonia Detection

Sayali Abhijeet Salkade^1∗, Sheetal Rathi¹

¹Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, 400101, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:12 April 2024, Accepted Date:04 June 2024, Published Date:25 June 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To create a deep learning system for pneumonia detection that is both effective and gradually optimized. Methods: A customized CNN is used with an incremental approach for pneumonia classification and detection. Starting with a baseline model, hypertuning parameters such as four convolution layers with filters of 16, 32, 64, and 128 sizes, a dropout layer with values of 0.3, 0.5, and 0.7, four batch normalization layers, and an Adam optimizer are added. A total images of 5,863 for training, 624 for testing, and 16 for validation from the Paul Mooney dataset were used to test the suggested model. Findings: The study recorded a test accuracy of 94% for the customized CNN followed by ResNet50 at 79.9%, VGG16 at 90.14%, VGG19 at 82.21%, InceptionV3 at 74.51%, and EfficientNetB1 at 83.17%. Recall of 98.20%, accuracy of 85.55%, AUC of 93.52%, and F1_score of 92.45% obtained were all fairly excellent for the customized CNN. 15 epochs, a learning rate of 0.0001, callbacks with a patience of 3, and an early stopping feature were applied to the training model. Novelty: Five convolution blocks, two separable convolution layers, one batch normalization layer, one maxpooling layer, and a fully connected layer with an Adam optimizer were all included in the customized CNN that was developed to identify and categorize pneumonia. With Explainable AI's GradCAM technology, pneumonia-infected areas on chest X-rays were highlighted and the sickness was seen.

Keywords: Customized CNN, VGG16, VGG19, ResNet50, Explainable AI

References

Ibrahim DM, Elshennawy NM, Sarhan AM. Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in Biology and Medicine. 2021;132:1–13. Available from: https://doi.org/10.1016/j.compbiomed.2021.104348
Wahid F, Azhar S, Ali S, Zia MS, Almisned FA, Gumaei A. Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine. Journal of Healthcare Engineering. 2022;2022:1–17. Available from: https://doi.org/10.1155/2022/1678000
Quan H, Xu X, Zheng T, Li Z, Zhao M, Cui X. DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network. Computers in Biology and Medicine. 2021;133:1–11. Available from: https://doi.org/10.1016/j.compbiomed.2021.104399
Alhudhaif A, Polat K, Karaman O. Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images. Expert Systems with Applications. 2021;180:1–9. Available from: https://doi.org/10.1016/j.eswa.2021.115141115141
Singh G, Yow KC. An Interpretable Deep Learning Model for Covid-19 Detection with Chest X-Ray Images. IEEE Access. 2021;9:85198–85208. Available from: https://doi.org/10.1109/ACCESS.2021.3087583
Horry MJ, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, et al. COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data. IEEE Access. 2020;8:149808–149824. Available from: https://doi.org/10.1109/access.2020.3016780
Wang J, Bao YM, Wen YF, Lu HB, Luo H, Xiang YF, et al. Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images. IEEE Transactions on Medical Imaging. 2020;39(8):2572–2583. Available from: https://doi.org/10.1109/TMI.2020.2994908
Wang Z, Xiao Y, Li Y, Zhang J, Lu F, Hou M, et al. Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays. Pattern Recognition. 2021;110:1–9. Available from: https://doi.org/10.1016/j.patcog.2020.107613
Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Computer Methods and Programs in Biomedicine. 2020;196:1–11. Available from: https://doi.org/10.1016/j.cmpb.2020.105608
Suryaa VS, R AXA, Aiswarya MS. Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images. International Journal of Advanced Computer Science and Applications. 2021;12(10):759–767. Available from: https://thesai.org/Downloads/Volume12No10/Paper_84-Efficient_DNN_Ensemble_for_Pneumonia_Detection.pdf
Hashmi FM, Katiyar S, Keskar AG, Bokde DN, Geem ZW. Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning. Diagnostics . 2020;10(6):1–23. Available from: https://doi.org/10.3390/diagnostics10060417
Yadav P, Menon N, Ravi V, Vishvanathan S. Unsupervised Representation Learning for Lung Disease Classification using Chest CT and X-ray Images. IEEE Transactions on Engineering Management . 2021;70(8):2774 –2786. Available from: https://doi.org/10.1109/TEM.2021.3103334
Jiang Z, Liu Y, Shao Z, Huang K. An Improved VGG16 Model for Pneumonia Image Classification. Applied Science. 2021;p. 1–19. Available from: https://doi.org/10.3390/app112311185
Mabrouk A, Redondo RPD, Dahou A, Elaziz MA, Kayed M. Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks. Applied Sciences. 2022;12(13):1–10. Available from: https://doi.org/10.3390/app12136448
Yao S, Chen Y, Tian X, Jiang R. Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN. Computational and Mathematical Methods in Medicine. 2021;2021:1–13. Available from: https://doi.org/10.1155/2021/8854892
Jain R, Nagrath P, Kataria G, Kaushik VS, Hemanth DJ. Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Measurement. 2020;165. Available from: https://doi.org/10.1016/j.measurement.2020.108046
Cha SM, Lee SS, Ko B. Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images. Applied Sciences. 2021;11(3):1–15. Available from: https://dx.doi.org/10.3390/app11031242
Wu L, Zhang J, Wang Y, Ding R, Cao Y, Liu G, et al. Pneumonia detection based on RSNA dataset and anchor-free deep learning detector. Scientific Reports. 2024;14(1):1–8. Available from: https://dx.doi.org/10.1038/s41598-024-52156-7
Guo K, Cheng J, Li K, Wang L, Lv Y, Cao D. Diagnosis and detection of pneumonia using weak-label based on X-ray images:a multi-center study. BMC Medical Imaging. 2023;23:1–8. Available from: https://doi.org/10.1186/s12880-023-01174-4
Almaslukh B. A Lightweight Deep Learning-Based Pneumonia Detection Approach for Energy-Efficient Medical Systems. Wireless Communications and Mobile Computing. 2021;2021:1–14. Available from: https://dx.doi.org/10.1155/2021/5556635
Salehi M, Mohammadi R, Ghaffari H, Sadighi N, Reiazi R. Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images. The British Journal of Radiology. 2021;94(1121). Available from: https://dx.doi.org/10.1259/bjr.20201263
Mahmud T, Rahman MA, Fattah SA. CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Computers in Biology and Medicine. 2020;122:1–10. Available from: https://dx.doi.org/10.1016/j.compbiomed.2020.103869
Kundu R, Das R, Geem ZW, Han GT, Sarkar R. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLOS ONE. 2021;16(9):1–29. Available from: https://dx.doi.org/10.1371/journal.pone.0256630
Hanif A, Zhang X, Wood S. A Survey on Explainable Artificial Intelligence Techniques and Challenges. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW). IEEE. 2021.

Copyright

© 2024 Salkade & Rathi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)