SIMD implementation of deep CNNs for myopia detection on a single-board computer system

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.289007

Keywords:

CNN, multi-core, cost-effective, platform, prediction, myopia, ocular, ODIR, AIoT, SIMD

Abstract

Deep learning algorithms, especially Convolution Neural Networks (CNN), have been rapidly developed due to their flexibility and scalability to be adopted in several fields for modeling real-world applications like object detection, image classification, etc. However, their high accuracy incurs intensive computations. Therefore, it is crucial to carefully choose a suitable computer platform and implementation methodology for CNN network architectures while achieving increased efficiency. Parallel architectures are prevalent in CNN implementation. Herein, we present a new Single Instruction Multi Data (SIMD) parallel implementation of the proposed CNN to speed up the execution process and make it suitable to deploy on low-cost, low-power consumption platforms. The proposed implementation produces an improved model of deep CNN executable on a cost-efficient platform and portability to work autonomously with multi-core processing units while maintaining working accuracy. Raspberry Pi 3 B is a low-power target device for implementing our model. The proposed approach is characterized by high diagnostic accuracy of up to 96.35 % while incurring power consumption of 3.65 Watts, achieving power reduction between 19.17 % and 68.45 % compared to the prior work. Meanwhile, it has a fine inference time for the selected platform. The outstanding results of this study reflect the success of employing parallel architectures to utilize the quad courses of the ARM processor on the target platform. The presented model can be an efficient medical assistant to provide automated detection and diagnosis for myopia ocular disease. Thus, it can be a promising healthcare toolkit that reduces the effort of the medical staff and increases the quality of the provided medical services for myopia patients

Author Biographies

Mamoon A Al Jbaar, Ninevah University

Master of Science in Computer Engineering, Assistant Lecturer

Department of Computer and Information Engineering

College of Electronics Engineering

Shefa A. Dawwd, University of Mosul

Professor of Computer Engineering, PhD

Department of Computer Engineering

References

  1. Suzen, A. A., Duman, B., Sen, B. (2020). Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). doi: https://doi.org/10.1109/hora49412.2020.9152915
  2. Choi, K., Sobelman, G. E. (2022). An Efficient CNN Accelerator for Low-Cost Edge Systems. ACM Transactions on Embedded Computing Systems, 21 (4), 1–20. doi: https://doi.org/10.1145/3539224
  3. Fernández-Cerero, D., Fernández-Rodríguez, J. Y., Álvarez-García, J. A., Soria-Morillo, L. M., Fernández-Montes, A. (2019). Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things. Sensors, 19 (13), 3026. doi: https://doi.org/10.3390/s19133026
  4. Saranya, V., Carmel Mary Belinda, M. J., Kanagachidambaresan, G. R. (2020). An Evolution of Innovations Protocols and Recent Technology in Industrial IoT. Internet of Things for Industry 4.0, 161–175. doi: https://doi.org/10.1007/978-3-030-32530-5_11
  5. Srinivasan, V., Meudt, S., Schwenker, F. (2019). Deep Learning Algorithms for Emotion Recognition on Low Power Single Board Computers. Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, 59–70. doi: https://doi.org/10.1007/978-3-030-20984-1_6
  6. Dubovečak, M., Dumić, E., Bernik, A. (2023). Face Detection and Recognition Using Raspberry PI Computer. Tehnički Glasnik, 17 (3), 346–352. doi: https://doi.org/10.31803/tg-20220321232047
  7. Zamir, M., Ali, N., Naseem, A., Ahmed Frasteen, A., Zafar, B., Assam, M., Othman, M., Attia, E.-A. (2022). Face Detection & Recognition from Images & Videos Based on CNN & Raspberry Pi. Computation, 10 (9), 148. doi: https://doi.org/10.3390/computation10090148
  8. Huang, Z., Yang, S., Zhou, M., Gong, Z., Abusorrah, A., Lin, C., Huang, Z. (2021). Making accurate object detection at the edge: review and new approach. Artificial Intelligence Review, 55 (3), 2245–2274. doi: https://doi.org/10.1007/s10462-021-10059-3
  9. Sonkar, S., Kumar, P., George, R. C., Yuvaraj, T. P., Philip, D., Ghosh, A. K. (2022). Real-Time Object Detection and Recognition Using Fixed-Wing LALE VTOL UAV. IEEE Sensors Journal, 22 (21), 20738–20747. doi: https://doi.org/10.1109/jsen.2022.3206345
  10. Didi, Z., El Azami, I., Boumait, E. M. (2022). Design of a Security System Based on Raspberry Pi with Motion Detection. Digital Technologies and Applications, 427–434. doi: https://doi.org/10.1007/978-3-031-02447-4_44
  11. Hammad, M., Abd El-Latif, A. A., Hussain, A., Abd El-Samie, F. E., Gupta, B. B., Ugail, H., Sedik, A. (2022). Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. Computers and Electrical Engineering, 100, 108011. doi: https://doi.org/10.1016/j.compeleceng.2022.108011
  12. Dhar, T., Dey, N., Borra, S., Sherratt, R. S. (2023). Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust. IEEE Transactions on Technology and Society, 4 (1), 68–75. doi: https://doi.org/10.1109/tts.2023.3234203
  13. Vayadande, K., Ingale, V., Verma, V., Yeole, A., Zawar, S., Jamadar, Z. (2022). Ocular Disease Recognition using Deep Learning. 2022 International Conference on Signal and Information Processing (IConSIP). doi: https://doi.org/10.1109/iconsip49665.2022.10007470
  14. Albahli, S., Ahmad Hassan Yar, G. N. (2022). Automated detection of diabetic retinopathy using custom convolutional neural network. Journal of X-Ray Science and Technology, 30 (2), 275–291. doi: https://doi.org/10.3233/xst-211073
  15. Ebri, A. E., Govender, P., Naidoo, K. S. (2019). Prevalence of vision impairment and refractive error in school learners in Calabar, Nigeria. African Vision and Eye Health, 78 (1). doi: https://doi.org/10.4102/aveh.v78i1.487
  16. Pakbin, M., Katibeh, M., Pakravan, M., Yaseri, M., Soleimanizad, R. (2015). Prevalence and causes of visual impairment and blindness in central Iran; The Yazd eye study. Journal of Ophthalmic and Vision Research, 10 (3), 279. doi: https://doi.org/10.4103/2008-322x.170362
  17. Gibertoni, G., Borghi, G., Rovati, L. (2022). Vision-Based Eye Image Classification for Ophthalmic Measurement Systems. Sensors, 23 (1), 386. doi: https://doi.org/10.3390/s23010386
  18. da Rocha, D. A., Ferreira, F. M. F., Peixoto, Z. M. A. (2022). Diabetic retinopathy classification using VGG16 neural network. Research on Biomedical Engineering, 38 (2), 761–772. doi: https://doi.org/10.1007/s42600-022-00200-8
  19. Pan, Y., Liu, J., Cai, Y., Yang, X., Zhang, Z., Long, H. et al. (2023). Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases. Frontiers in Physiology, 14. doi: https://doi.org/10.3389/fphys.2023.1126780
  20. Menghani, G. (2023). Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better. ACM Computing Surveys, 55 (12), 1–37. doi: https://doi.org/10.1145/3578938
  21. Islam, S., Deng, J., Zhou, S., Pan, C., Ding, C., Xie, M. (2022). Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). doi: https://doi.org/10.23919/date54114.2022.9774756
  22. Dai, S., Chen, L., Lei, T., Zhou, C., Wen, Y. (2020). Automatic Detection Of Pathological Myopia And High Myopia On Fundus Images. 2020 IEEE International Conference on Multimedia and Expo (ICME). doi: https://doi.org/10.1109/icme46284.2020.9102787
  23. Gour, N., Khanna, P. (2021). Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomedical Signal Processing and Control, 66, 102329. doi: https://doi.org/10.1016/j.bspc.2020.102329
  24. Topaloglu, I. (2022). Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification. Scientia Iranica, 30 (5), 1731–1742. doi: https://doi.org/10.24200/sci.2022.58049.5537
  25. Rakhmetulayeva, S., Syrymbet, Z. (2022). Implementation of convolutional neural network for predicting glaucoma from fundus images. Eastern-European Journal of Enterprise Technologies, 6 (2 (120)), 70–77. doi: https://doi.org/10.15587/1729-4061.2022.269229
  26. David, S. A., Mahesh, C., Kumar, V. D., Polat, K., Alhudhaif, A., Nour, M. (2022). Retinal Blood Vessels and Optic Disc Segmentation Using U-Net. Mathematical Problems in Engineering, 2022, 1–11. doi: https://doi.org/10.1155/2022/8030954
  27. Wang, K., Xu, C., Li, G., Zhang, Y., Zheng, Y., Sun, C. (2023). Combining convolutional neural networks and self-attention for fundus diseases identification. Scientific Reports, 13 (1). doi: https://doi.org/10.1038/s41598-022-27358-6
  28. Maqsood, Z., Gupta, M. K. (2022). Automatic Detection of Diabetic Retinopathy on the Edge. Cyber Security, Privacy and Networking, 129–139. doi: https://doi.org/10.1007/978-981-16-8664-1_12
  29. Karamihan, K. C., Agustino, I. D. F., Bionesta, R. B. B., Tuason, F. C., Arellano, S. V. E., Esguerra, P. A. M. (2019). SBC-Based Cataract Detection System using Deep Convolutional Neural Network with Transfer Learning Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 9(2), 4605–4613. doi: https://doi.org/10.35940/ijrte.b3368.078219
  30. Civit-Masot, J., Luna-Perejón, F., Corral, J. M. R., Domínguez-Morales, M., Morgado-Estévez, A., Civit, A. (2021). A study on the use of Edge TPUs for eye fundus image segmentation. Engineering Applications of Artificial Intelligence, 104, 104384. doi: https://doi.org/10.1016/j.engappai.2021.104384
  31. Lee, S.-J., Park, S.-S., Chung, K.-S. (2018). Efficient SIMD implementation for accelerating convolutional neural network. Proceedings of the 4th International Conference on Communication and Information Processing. doi: https://doi.org/10.1145/3290420.3290444
  32. Raspberry Pi 3 Model B. Available at: https://www.raspberrypi.com/products/raspberry-pi-3-model-b/
  33. Raspberry Pi Power Consumption Guide. Available at: https://www.ecoenergygeek.com/raspberry-pi-power-consumption/
  34. Wang, J., Yang, L., Huo, Z., He, W., Luo, J. (2020). Multi-Label Classification of Fundus Images With EfficientNet. IEEE Access, 8, 212499–212508. doi: https://doi.org/10.1109/access.2020.3040275
  35. He, J., Li, C., Ye, J., Qiao, Y., Gu, L. (2021). Multi-label ocular disease classification with a dense correlation deep neural network. Biomedical Signal Processing and Control, 63, 102167. doi: https://doi.org/10.1016/j.bspc.2020.102167
  36. Bhati, A., Gour, N., Khanna, P., Ojha, A. (2023). Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset. Computers in Biology and Medicine, 153, 106519. doi: https://doi.org/10.1016/j.compbiomed.2022.106519
  37. Jeny, A. A., Junayed, M. S., Islam, M. B. (2023). Deep Neural Network-Based Ensemble Model for Eye Diseases Detection and Classification. Image Analysis & Stereology, 42 (2), 77–91. doi: https://doi.org/10.5566/ias.2857
  38. Kristiani, E., Yang, C.-T., Huang, C.-Y. (2020). iSEC: An Optimized Deep Learning Model for Image Classification on Edge Computing. IEEE Access, 8, 27267–27276. doi: https://doi.org/10.1109/access.2020.2971566
  39. Goel, A., Aghajanzadeh, S., Tung, C., Chen, S.-H., Thiruvathukal, G. K., Lu, Y.-H. (2020). Modular Neural Networks for Low-Power Image Classification on Embedded Devices. ACM Transactions on Design Automation of Electronic Systems, 26 (1), 1–35. doi: https://doi.org/10.1145/3408062
  40. Dong, Z., Li, N., Iosifidis, A., Zhang, Q. (2022). Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing. arXiv. doi: https://doi.org/10.48550/arXiv.2211.13778
  41. James, N., Ong, L.-Y., Leow, M.-C. (2022). Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster. Future Internet, 14 (8), 220. doi: https://doi.org/10.3390/fi14080220
SIMD implementation of deep CNNs for myopia detection on a single-board computer system

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Published

2023-10-31

How to Cite

Al Jbaar, M. A., & Dawwd, S. A. (2023). SIMD implementation of deep CNNs for myopia detection on a single-board computer system. Eastern-European Journal of Enterprise Technologies, 5(9 (125), 98–108. https://doi.org/10.15587/1729-4061.2023.289007

Issue

Vol. 5 No. 9 (125) (2023): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2023 Mamoon A Al Jbaar, Shefa A. Dawwd

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