Pneumonia Detection in Chest X-Rays using Transfer Learning and TPUs
Received: 27 August 2023 | Revised: 6 September 2023 | Accepted: 9 September 2023 | Online: 20 September 2023
Corresponding author: Bellary Chiterki Anil
Abstract
Pneumonia is a severe respiratory disease with potentially life-threatening consequences if not promptly diagnosed and treated. Chest X-rays are commonly employed for pneumonia detection, but interpreting the images can pose challenges. This study explores the efficacy of four popular transfer learning models, namely VGG16, ResNet, InceptionNet, and DenseNet, alongside a custom CNN model for this task. The model performance is evaluated using Mean Absolute Error (MAE) as the performance metric. The findings reveal that VGG16 outperforms the other transfer learning models, achieving the lowest MAE (66.19). To optimize the model training process, a distributed training strategy utilizing TensorFlow's TPU (Tensor Processing Unit) strategy is implemented. The custom CNN model is parallelized using TPU's multiple instances available over the cloud, enabling efficient computation parallelization and significantly reducing model training times. The experimental results demonstrate a remarkable decrease of 68.36% and 54.74% in model training times for the CNN model when trained using TPU compared to training on a CPU and GPU, respectively.
Keywords:
pneumonia detection, chest X-ray images, deep learning, transfer learning, TPU, distributed trainingDownloads
References
S. Kalgutkar et al., "Pneumonia Detection from Chest X-ray using Transfer Learning," in 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, Apr. 2021.
S. V. Militante and B. G. Sibbaluca, "Pneumonia Detection Using Convolutional Neural Networks," International Journal of Scientific & Technology Research, vol. 9, no. 4, pp. 1332–1337, 2020.
R. Kundu, R. Das, Z. W. Geem, G.-T. Han, and R. Sarkar, "Pneumonia detection in chest X-ray images using an ensemble of deep learning models," PLOS ONE, vol. 16, no. 9, 2021, Art. no. e0256630.
A. Ranjan, C. Kumar, R. K. Gupta, and R. Misra, "Transfer Learning Based Approach for Pneumonia Detection Using Customized VGG16 Deep Learning Model," in Internet of Things and Connected Technologies, Cham, 2022, pp. 17–28.
T. Rahman et al., "Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray," Applied Sciences, vol. 10, no. 9, Jan. 2020, Art. no. 3233.
N. C. Kundur and P. B. Mallikarjuna, "Deep Convolutional Neural Network Architecture for Plant Seedling Classification," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9464–9470, Dec. 2022.
F. Mlawa, E. Mkoba, and N. Mduma, "A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10856–10860, Jun. 2023.
A. M. Ali, K. Ghafoor, A. Mulahuwaish, and H. Maghdid, "COVID-19 pneumonia level detection using deep learning algorithm and transfer learning," Evolutionary Intelligence, Sep. 2022.
D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, "Pneumonia Detection Using CNN based Feature Extraction," in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, Oct. 2019.
P. Pattrapisetwong and W. Chiracharit, "Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding," in 2016 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand, Sep. 2016.
S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, "Disease Detection in Apple Leaves Using Image Processing Techniques," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, Apr. 2022.
R. H. Mwawado, B. J. Maiseli, and M. A. Dida, "Robust Edge Detection Method for Segmentation of Diabetic Foot Ulcer Images," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6034–6040, Aug. 2020.
N. C. Kundur and P. B. Mallikarjuna, "Insect Pest Image Detection and Classification using Deep Learning," International Journal of Advanced Computer Science and Applications, vol. 13, no. 9, pp. 411–421, 2022.
P. Rajpurkar et al., "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning." arXiv, Dec. 25, 2017.
N. C. Kundur and P. B. Mallikarjuna, "Ensemble Efficient Net and ResNet model for Crop Disease Identification," International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 4, pp. 378–390, Dec. 2022.
V. Sirish Kaushik, A. Nayyar, G. Kataria, and R. Jain, "Pneumonia Detection Using Convolutional Neural Networks (CNNs)," in Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), Singapore, 2020, pp. 471–483.
Downloads
How to Cite
License
Copyright (c) 2023 Niranjan C. Kundur, Bellary Chiterki Anil, Praveen M. Dhulavvagol, Renuka Ganiger, Balakrishnan Ramadoss
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
