Abstract
Effective detection of the COVID-19 pandemic is essential for timely disease treatment and prevention. This work studies compact deep-learning models executed on mobile devices for segmenting COVID-19 RT-PCR test tube images, a crucial image-processing step preceding higher-level tasks. Since the device resource constraints and the need for rapid results necessitate compact and streamlined models with reasonable accuracy, we employ the hyperparameter width multiplier \(\alpha \) to the trainable components in the two deep learning models based on the U-Net architecture, including MobileNetV2 and Xception. Our new compact models, called \(\alpha \)-MobileNetV2 and \(\alpha \)-Xception, facilitate the progressive simplification of the U-Net model structures, maintaining high accuracy. By varying the width multiplier \(\alpha \), we explore diverse training conditions for the models, analyzing the model size and its performance. The final model achieves a \(3.2 \times \) reduction in size and \(3 \times \) faster inference, with merely a 1.2% loss in accuracy compared to standard MobileNetV2 on segmenting COVID-19 PCR test tube images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anisuzzaman, D.M., Patel, Y., Niezgoda, J.A., Gopalakrishnan, S., Yu, Z.: A mobile app for wound localization using deep learning. IEEE Access 10, 61398–61409 (2022)
Castanyer, R.C., Martínez-Fernández, S., Franch, X.: Integration of convolutional neural networks in mobile applications. In: 1st IEEE/ACM Workshop on AI Engineering - Software Engineering for AI, WAIN@ICSE 2021, Madrid, Spain, 30–31 May 2021, pp. 27–34 (2021)
Chen, Y., Yang, T., Emer, J.S., Sze, V.: Eyeriss v2: a flexible accelerator for emerging deep neural networks on mobile devices. IEEE J. Emerg. Sel. Topics Circuits Syst. 9(2), 292–308 (2019)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR, pp. 1800–1807 (2017)
Gutierrez-Lazcano, L., Camacho-Bello, C.J., Cornejo-Velazquez, E., Arroyo-Nunez, J.H., Clavel-Maqueda, M.: Cuscuta spp. segmentation based on unmanned aerial vehicles (UAVs) and orthomasaics using a U-net xception-style model. Remote Sens. 14(17), 4315 (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Heithoff, D.M., et al.: Assessment of a smartphone-based loop-mediated isothermal amplification assay for detection of SARS-CoV-2 and influenza viruses. JAMA Netw. Open 5(1), e2145669–e2145669 (2022)
Hossain, M.D., Chen, D.: Segmentation for object-based image analysis (OBIA): a review of algorithms and challenges from remote sensing perspective. ISPRS J. Photogramm. Remote. Sens. 150, 115–134 (2019)
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861 (2017)
Huang, L., Ruan, S., Denoeux, T.: Application of belief functions to medical image segmentation: a review. Inf. Fusion 91, 737–756 (2023)
Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the U-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)
Khan, M.Z., Gajendran, M.K., Lee, Y., Khan, M.A.: Deep neural architectures for medical image semantic segmentation: review. IEEE Access 9, 83002–83024 (2021)
Khan, S.H., Rahmani, H., Shah, S.A.A., Bennamoun, M.: A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool Publishers, San Rafael (2018)
Kirillov, A., et al.: Segment anything. CoRR abs/2304.02643 (2023)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Lei, T.: Image Segmentation: Principles, Techniques, and Applications. Hoboken, NJ (2023)
Lucas, A.M., Ryder, P., Li, B., Cimini, B.A., Eliceiri, K.W., Carpenter, A.E.: Open-source deep-learning software for bioimage segmentation. Mol. Biol. Cell 32(9), 823–829 (2021)
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523–3542 (2022)
Mo, Y., Wu, Y., Yang, X., Liu, F., Liao, Y.: Review the state-of-the-art technologies of semantic segmentation based on deep learning. Neurocomputing 493, 626–646 (2022)
Mukherjee, G., Chatterjee, A., Tudu, B.: Identification of the types of disease for tomato plants using a modified gray wolf optimization optimized mobilenetv2 convolutional neural network architecture driven computer vision framework. Concurr. Comput. Pract. Exp. 34(22) (2022)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: CVPR, pp. 4510–4520 (2018)
Summers, G., Lim, A., Wheeler, A.J.: A characterisation of benthic currents from seabed bathymetry: an object-based image analysis of cold-water coral mounds. Remote. Sens. 14(19), 4731 (2022)
Torres, R.N., Fraternali, P., Romero, J.: ODIN: an object detection and instance segmentation diagnosis framework. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020, Part VI. LNCS, vol. 12540, pp. 19–31. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65414-6_3
Wu, X., Hong, D., Chanussot, J.: UIU-NET: U-net in U-net for infrared small object detection. IEEE Trans. Image Process. 32, 364–376 (2023)
Yang, Q., Zhang, Y., Dai, W., Pan, S.J.: Transfer Learning in Computer Vision, pp. 221–233. Cambridge University Press, Cambridge (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xiang, T., Dean, R., Zhao, J., Pham, N. (2024). On Deploying Mobile Deep Learning to Segment COVID-19 PCR Test Tube Images. In: Yan, W.Q., Nguyen, M., Nand, P., Li, X. (eds) Image and Video Technology. PSIVT 2023. Lecture Notes in Computer Science, vol 14403. Springer, Singapore. https://doi.org/10.1007/978-981-97-0376-0_30
Download citation
DOI: https://doi.org/10.1007/978-981-97-0376-0_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0375-3
Online ISBN: 978-981-97-0376-0
eBook Packages: Computer ScienceComputer Science (R0)