Vol. 28 No. 1 (2025) Cover Image
Vol. 28 No. 1 (2025)

Published: March 31, 2025

Pages: 97-120

Articles

Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification

Yasir Salam Abdulghafoor 1 Auns Qusai Al-Neami 1 Ahmed Faeq Hussein 1
1 Dept Biomedical Eng., College of Engineering, Al-Nahrain University
History
  • Received: August 13, 2024
  • Revised: September 8, 2024
  • Accepted: October 20, 2024
  • Available Online: March 20, 2025
DOI

https://doi.org/10.29194/NJES.2801097

Abstract

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.

Keywords

References

  1. R. Sharma, "Mapping of global, regional and national incidence, mortality and mortality-to-incidence ratio of lung cancer in 2020 and 2050," International Journal of Clinical Oncology, vol. 27, no. 4, pp. 665-675, 2022.
  2. W. H. O. C. I. W. H. O. https://www.who.int/news-room/fact-sheets/detail/cancer. (Accessed 22 Jan 2024).
  3. A. J. Alberg and J. M. Samet, "Epidemiology of lung cancer," Chest, vol. 123, no. 1, pp. 21S-49S, 2003.
  4. U. S. E. P. A. I. E. Division, A Citizen's guide to radon: the guide to protecting yourself and your family from radon. US Environmental Protection Agency, Indoor Environments Division, 2002.
  5. M. Šutić et al., "Diagnostic, predictive, and prognostic biomarkers in non-small cell lung cancer (NSCLC) management," Journal of personalized medicine, vol. 11, no. 11, p. 1102, 2021.
  6. S. H. Song, C. W. Ha, C. Kim, and G. M. Seong, "Complete spontaneous remission of small cell lung cancer in the absence of specific treatment: A case report," Thoracic Cancer, vol. 12, no. 19, pp. 2611-2613, 2021.
  7. R. Advani et al., "Phase I and pharmacokinetic study of BMS-188797, a new taxane analog, administered on a weekly schedule in patients with advanced malignancies," Clinical cancer research, vol. 9, no. 14, pp. 5187-5194, 2003.
  8. E. S. Kim et al., "The BATTLE trial: personalizing therapy for lung cancer," Cancer discovery, vol. 1, no. 1, pp. 44-53, 2011.
  9. M. Ladanyi and W. Pao, "Lung adenocarcinoma: guiding EGFR-targeted therapy and beyond," Modern pathology, vol. 21, no. 2, pp. S16-S22, 2008.
  10. A. Pallis et al., "Targeted therapies in the treatment of advanced/metastatic NSCLC," European journal of cancer, vol. 45, no. 14, pp. 2473-2487, 2009.
  11. S. Walters et al., "Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: a population-based study, 2004–2007," Thorax, vol. 68, no. 6, pp. 551-564, 2013.
  12. K. A. Tran, O. Kondrashova, A. Bradley, E. D. Williams, J. V. Pearson, and N. Waddell, "Deep learning in cancer diagnosis, prognosis and treatment selection," Genome Medicine, vol. 13, pp. 1-17, 2021.
  13. S. Benzekry, "Artificial intelligence and mechanistic modeling for clinical decision making in oncology," Clinical Pharmacology & Therapeutics, vol. 108, no. 3, pp. 471-486, 2020.
  14. P. N. Srinivasu, J. G. SivaSai, M. F. Ijaz, A. K. Bhoi, W. Kim, and J. J. Kang, "Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM," Sensors, vol. 21, no. 8, p. 2852, 2021.
  15. I. El Naqa and M. J. Murphy, What is machine learning? Springer, 2015.
  16. M. M. e. al, "Adaptive Computation and Machine Learning," MIT PRESS.Cambridge 2018.
  17. U. Nations. "The Sustainable Development Goals Report." https://sdgs.un.org/goals (accessed 22/3, 2024).
  18. V. Janapa Reddi et al., "Edge impulse: An mlops platform for tiny machine learning," Proceedings of Machine Learning and Systems, vol. 5, 2023.
  19. H. Du, Deep Learning Techniques for Analyzing Clinical Lung Cancer Data. Wake Forest University, 2019.
  20. M. A. Thanoon, M. A. Zulkifley, M. A. A. Mohd Zainuri, and S. R. Abdani, "A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images," Diagnostics, vol. 13, no. 16, p. 2617, 2023.
  21. X. Chen, L. Yao, T. Zhou, J. Dong, and Y. Zhang, "Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images," Pattern recognition, vol. 113, p. 107826, 2021.
  22. A. T. Sadiq and S. M. Abdullah, "Hybrid intelligent technique for text categorization," in 2012 international conference on advanced computer science applications and technologies (ACSAT), 2012: IEEE, pp. 238-245.
  23. D. Ezzat and H. A. Ella, "GSA-DenseNet121-COVID-19: a hybrid deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization algorithm," arXiv preprint arXiv:2004.05084, 2020.
  24. G. Savitha and P. Jidesh, "A holistic deep learning approach for identification and classification of sub-solid lung nodules in computed tomographic scans," Computers & Electrical Engineering, vol. 84, p. 106626, 2020.
  25. H. N. Abdullah and H. K. Abduljaleel, "Deep CNN based skin lesion image denoising and segmentation using active contour method," Engineering and Technology Journal, vol. 37, no. 11A, pp. 464-469, 2019.
  26. K. He et al., "Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images," Pattern recognition, vol. 113, p. 107828, 2021.
  27. M. R. Regmi et al., "An aggressive progression of a lung mass: a rare case of sarcomatoid carcinoma," European Journal of Medical Case Reports, vol. 4, no. 5, pp. 177-179, 2020.
  28. J. Biederer et al., "MRI of the lung (3/3)—current applications and future perspectives," Insights into imaging, vol. 3, pp. 373-386, 2012.
  29. A. T. Abdulahi, R. O. Ogundokun, A. R. Adenike, M. A. Shah, and Y. K. Ahmed, "PulmoNet: a novel deep learning based pulmonary diseases detection model," BMC Medical Imaging, vol. 24, no. 1, p. 51, 2024.
  30. H. H. Abid and M. E. Abdulmunim, "Segmentation brain tumor and diagnosing using watershed algorithm," Am. J. Eng. Res, vol. 5, no. 11, pp. 31-35, 2016.
  31. S. T. Ahmed and S. M. Kadhem, "Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey," International Journal of Interactive Mobile Technologies, vol. 15, no. 16, 2021.
  32. A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, and J. J. Rodrigues, "Identifying pneumonia in chest X-rays: A deep learning approach," Measurement, vol. 145, pp. 511-518, 2019.
  33. A. S. Abdalrada, O. H. Yahya, A. H. M. Alaidi, N. A. Hussein, H. T. Alrikabi, and T. A.-Q. Al-Quraishi, "A predictive model for liver disease progression based on logistic regression algorithm," Periodicals of Engineering and Natural Sciences, vol. 7, no. 3, pp. 1255-1264, 2019.
  34. E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, and M. Z. Parvez, "CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images," Chaos, Solitons & Fractals, vol. 142, p. 110495, 2021.
  35. 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, p. 3233, 2020.
  36. A. K. Das, S. Kalam, C. Kumar, and D. Sinha, "TLCoV-An automated Covid-19 screening model using Transfer Learning from chest X-ray images," Chaos, Solitons & Fractals, vol. 144, p. 110713, 2021.
  37. P. Rajesh, A. Murugan, B. Murugamantham, and S. Ganesh, "Lung cancer diagnosis and treatment using AI and Mobile applications," 2020.
  38. S. T. A. S. M. K, "Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey’," IJIM, vol. 15, no. 16, 2021.
  39. K. Punithavathy, S. Poobal, and M. Ramya, "Performance evaluation of machine learning techniques in lung cancer classification from PET/CT images," FME Transactions, vol. 47, no. 3, pp. 418-423, 2019.
  40. A. Y. Saleh, C. K. Chin, V. Penshie, and H. R. H. Al-Absi, "Lung cancer medical images classification using hybrid CNN-SVM," International Journal of Advances in Intelligent Informatics, vol. 7, no. 2, pp. 151-162, 2021.
  41. J. Morgado et al., "Machine learning and feature selection methods for egfr mutation status prediction in lung cancer," Applied Sciences, vol. 11, no. 7, p. 3273, 2021.
  42. T Zhang et al, ",’ Simultaneous Identification of EGFR, KRAS, ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics’ " MDPI, 2021, doi: . https:// doi.org/10.3390/cancers13081814,2021.
  43. L. Hussain et al., "Lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features," Applied Sciences, vol. 12, no. 13, p. 6517, 2022.
  44. J. Perumal, P. Lee, K. Dev, H. Q. Lim, U. Dinish, and M. Olivo, "Machine Learning Assisted Real-Time Label-Free SERS Diagnoses of Malignant Pleural Effusion due to Lung Cancer," Biosensors, vol. 12, no. 11, p. 940, 2022.
  45. S Ishii et al, "’ Machine learning-based gene alteration prediction model for primary lung cancer using cytologic images," ’, in Wiley Online Library (wileyonlinelibrary.com), 2022.
  46. F. Carrillo-Perez, J. C. Morales, D. Castillo-Secilla, O. Gevaert, I. Rojas, and L. J. Herrera, "Machine-learning-based late fusion on multi-omics and multi-scale data for non-small-cell lung cancer diagnosis," Journal of Personalized Medicine, vol. 12, no. 4, p. 601, 2022.
  47. P Nancy et al, "’ Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection’," International Journal of Electrical and Electronics Research (IJEER), 0822SI-IJEER-2022-05;,, 2022.
  48. H J Kwon et al, " Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques’," MDPI Journal,, ,2023, doi: ,. https:// doi.org/10.3390/cancers15184556.
  49. Q. Huang et al., "Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data," Diagnostics, vol. 13, no. 4, p. 648, 2023.
  50. MS Kumar&KV Rao, " A Labelled Priority based Weighted Classifier for Feature Extraction for Accurate Lung Tumour Detection using Machine Learning Technique," ’, International Journal of Intelligent Systems and Applications in Engineering,, 2023.
  51. A Earnest et al, " Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients’,", Healthcare ,2023, doi: ’ https:// doi.org/10.3390/healthcare11202756.
  52. Kumar Mohan &Bhraguram Thayyile, "Machine learning techniques for lung cancer risk prediction for text dataset’," ’,international journal of data informatics and intelligent computing’,, 2023.
  53. M Dirik, " Machine learning-based lung cancer diagnosis’." Turkish Journal of Engineering, 2023, doi: https://dergipark.org.tr/en/pub/tuje ,.
  54. M. S. Bhuiyan et al., "Advancements in early detection of lung cancer in public health: a comprehensive study utilizing machine learning algorithms and predictive models," Journal of Computer Science and Technology Studies, vol. 6, no. 1, pp. 113-121, 2024.
  55. Y Xu et al, "’ Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging," Clinical cancer research, June 1, 2019, doi: , doi: 10.1158/1078-0432.CCR-18-2495, Clin Cancer Res; 25(11).
  56. G Jakimovisky & D Davcev, "Using Double Convolution Neural Network for Lung Cancer Stage Detection’," Appl. Sci., 2019, doi: doi:10.3390/app9030427,.
  57. H Park & C Monahan, "’ Genetic Deep Learning for Lung Cancer Screening," arXiv: , vol. v1, 27 Jul 2019.
  58. RR Subramanian et al’, ",’ Lung Cancer Prediction Using Deep Learning Framework," International Journal of Control and Automation, vol., Vol. 13, no., No. 3,, pp. pp. 154-160, (2020).
  59. HF Al-Yasriy et al, " Diagnosis of Lung Cancer Based on CT Scans Using CNN’,," in 2nd International Scientific Conference of Al-Ayen University (ISCAU-2020), 2020.
  60. A Elnakib et al, "Early Lung Cancer Detection Using Deep Learning Optimization’," IJOE, vol. Vol. 16,, no. No. 6, 2020.
  61. C. L. e. al’;, "Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images’," Appl. Sci., 2020, doi: doi:10.3390/app10072591,.
  62. A. K. e. al’, "An Approach for Lung Cancer Detection using Deep Learning," ’, International Research Journal of Engineering and Technology (IRJET),, vol., Volume: 07, no. Issue: 09 | Sep 2020.
  63. - TA Amma et al, ",’ Lung Cancer Identification and Prediction Based on VGG Architecture’," International Journal of Research in Engineering, Science and Management, vol. Volume-3, no., Issue-7, , July-2020.
  64. X Zhan, . , "’ A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer’," Sensors, 2021,, doi: https://doi.org/ 10.3390/s21237996,.
  65. SHM Mohammed& ACinar’, ",’ Lung cancer classification with Convolutional Neural Network Architectures," Qubahan academic Journal,, 2021, doi: https://doi.org/10.48161/qaj.v1n1a33.
  66. A. A. Abd Al-Ameer, G. A. Hussien, and H. A. Al Ameri, "Lung cancer detection using image processing and deep learning," Indonesian Journal of Electrical Engineering and Computer Science, vol. 28, no. 2, pp. 987-993, 2022.
  67. Z Ren et al, 1614., "’ A Hybrid Framework for Lung Cancer Classification’,," Electronics, , 2022, doi: https://doi.org/10.3390/ electronics11101614,.
  68. M Humayun et al, "’ A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma’,," Healthcare, 2022,, doi: https:// doi.org/10.3390/healthcare10061058.
  69. Y. Said, A. A. Alsheikhy, T. Shawly, and H. Lahza, "Medical images segmentation for lung cancer diagnosis based on deep learning architectures," Diagnostics, vol. 13, no. 3, p. 546, 2023.
  70. S. Üzülmez and M. A. Çifçi, "Early diagnosis of lung cancer using deep learning and uncertainty measures," Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 39, no. 1, pp. 385-400, 2024.
  71. N Vijayan & J Kuruvilla " The impact of transfer learning on lung cancer detection using various deep neural network architectures’,," presented at the 19th India Council International Conference (INDICON),, 2022.
  72. M Agarwal et al, "An Efficient and Optimized Convolution Neural Network for Covid and Lung Disease Detection’,," presented at the 8th International Conference on Communication and Electronics,, 2023.
  73. D. Al-Naqeeb and O. Al-Shamma, "DuaNet: A Novel Lightweight CNN Model for Classifying Five-class Lung Diseases," in 2022 International Conference on Data Science and Intelligent Computing (ICDSIC), 2022: IEEE, pp. 202-207.
  74. S. S. Skandha, M. Agarwal, K. Utkarsh, S. K. Gupta, V. K. Koppula, and J. S. Suri, "A novel genetic algorithm-based approach for compression and acceleration of deep learning convolution neural network: an application in computer tomography lung cancer data," Neural Computing and Applications, vol. 34, no. 23, pp. 20915-20937, 2022.
  75. P. Xiao et al., "FastNet: A Lightweight Convolutional Neural Network for Tumors Fast Identification in Mobile Computer-Assisted Devices," IEEE Internet of Things Journal, 2023.
  76. S.A. R. ZAIDI et al, " Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)’,," Digital Object Identifier 10.1109/ACCESS.2022.3207200,, 2022.
  77. e. a. R. David, '' , , , "TensorFlow lite micro: Embedded machine learning for TinyML systems," in in Proc. Mach. Learn. Syst, 2021., vol. vol. 3, p. pp. 800811.
  78. L.CAPOGROSSO et al, " A Machine Learning-oriented Survey on Tiny Machine Learning’," arXiv:IEEE, vol.,v2, ,26 Sep 2023.
  79. P. Warden and D. Situnayake, "Tinyml: Machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers.," O’Reilly Media,, 2019.
  80. H. S. N. - A. Pramod, and A. K. Tyagi, , "‘Machine learning and deep learning: Open issues and future research directions for the next 10 years,’" ‘’ Computational analysis and deep learning for medical care: Principles, methods, and applications,, pp. pp. 463–490, , 2021.
  81. e. a. N. C. Thompson, "The computational limits of deep learning,’’," arXiv preprint arXiv:2007.05558,, 2020.
  82. L. Dutta and S. Bharali, "Tinyml meets iot: A comprehensive survey," Internet of Things, vol. 16, p. 100461, 2021.
  83. V. Rajapakse, I. Karunanayake, and N. Ahmed, "Intelligence at the extreme edge: A survey on reformable tinyml," ACM Computing Surveys, vol. 55, no. 13s, pp. 1-30, 2023.
  84. L. Capogrosso, F. Cunico, D. S. Cheng, F. Fummi, and M. Cristani, "A machine learning-oriented survey on tiny machine learning," IEEE Access, 2024.
  85. H. Ren, D. Anicic, and T. A. Runkler, "TinyReptile: TinyML with federated meta-learning," in 2023 International Joint Conference on Neural Networks (IJCNN), 2023: IEEE, pp. 1-9.
  86. e. a. - L. Heim, " ‘‘Measuring what really matters: Optimizing neural networks for tinyml,’’," arXiv preprint arXiv:2104.10645, , 2021.
  87. J. Chang, Y. Choi, T. Lee, and J. Cho, "Reducing MAC operation in convolutional neural network with sign prediction," in 2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018: IEEE, pp. 177-182.
  88. M. Olyaiy, C. Ng, and M. Lis, "Accelerating DNNs inference with predictive layer fusion," in Proceedings of the ACM International Conference on Supercomputing, 2021, pp. 291-303.
  89. e. a. - W.-C. Lin, "‘‘An efficient and low-power mlp accelerator architecture supporting structured pruning, sparse activations and asymmetric quantization for edge computing,," presented at the 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), in 2021.
  90. e. a. Y.-C. Zhou, "‘‘An enhanced data cache with in-cache processing units for convolutional neural network accelerators,’’ " presented at the 15th International Conference on Solid- State & Integrated Circuit Technology (ICSICT). , in 2020.
  91. e. a. - F. Conti, "‘‘Energy-efficient vision on the pulp platform for ultra-low power parallel computing,’’," Workshop on Signal Processing Systems (SiPS). IEEE,, pp. pp. 1–6., in 2014
  92. A. Garofaloet al ‘‘, "Pulp-nn: accelerating quantized neural networks on parallel ultra-low-power risc-v processors,," ’ Philosophical Transactions of the Royal Society A, vol. vol. 378,, no. no. 2164, pp. ’, , p. 20190155, 2020.
  93. P.-n. A Garofalo et al, "A computing library for quantized neural network inference at the edge on risc-v based parallel ultra low power clusters,’" presented at the 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS), in 2019.
  94. S. Prakash et al., "CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (TinyML) Acceleration on FPGAs," in 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 23-25 April 2023 2023, pp. 157-167, doi: 10.1109/ISPASS57527.2023.00024.
  95. C. Zhou et al., "ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator," IEEE Micro, vol. 42, no. 6, pp. 76-87, 2022, doi: 10.1109/MM.2022.3198321.
  96. R. Immonen and T. Hämäläinen, "Tiny Machine Learning for Resource-Constrained Microcontrollers," Journal of Sensors, vol. 2022, p. 7437023, 2022/11/10 2022, doi: 10.1155/2022/7437023.
  97. e. a. Zhuang Liu, "Learning
  98. efficient convolutional networks through network slimming," in In ICCV, , 2017.
  99. H. Ren, D. Anicic, and T. A. Runkler, "TinyOL: TinyML with Online-Learning on Microcontrollers," in 2021 International Joint Conference on Neural Networks (IJCNN), 18-22 July 2021 2021, pp. 1-8, doi: 10.1109/IJCNN52387.2021.9533927.
  100. K. Dokic, M. Martinovic, and D. Mandusic, "Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework," in 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 25-27 Sept. 2020 2020, pp. 1-6, doi: 10.1109/SEEDA-CECNSM49515.2020.9221846.
  101. e. a. - Y. Zhang, "“Hello edge: keyword spotting on microcontrollers,," http://arxiv.org/ abs/1711.07128., ” 2017.
  102. "ARM Cortex-M,." Wikipedia. (accessed.
  103. M. Courbariaux, Y. Bengio, and J.-P. David, "Training deep neural networks with low precision multiplications," arXiv preprint arXiv:1412.7024, 2014.
  104. C. Zhang, "How to run deep learning model on microcontroller with CMSIS-NN (part 3),," https://www.dlology.com/ b l o g / h o w t o - r u n - d e e p - l e a r n i n g - m o d e l - o n -microcontrollerwith-cmsis-nn-part-3/. “” 2018.
  105. D. Lin, S. Talathi, and S. Annapureddy, "Fixed Point Quantization of Deep Convolutional Networks," presented at the Proceedings of The 33rd International Conference on Machine Learning, Proceedings of Machine Learning Research, 2016. [Online]. Available: https://proceedings.mlr.press/v48/linb16.html.
  106. e. a. - S. Zhuo, , , "“An empirical study of low precision quantization for TinyML," http://arxiv.org/abs/2203.05492, ” 2022,.
  107. A. Capotondi, M. Rusci, M. Fariselli, and L. Benini, "CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 5, pp. 871-875, 2020, doi: 10.1109/TCSII.2020.2983648.
  108. H. A. Rashid, P. R. Ovi, C. Busart, A. Gangopadhyay, and T. Mohsenin, "TinyM(2)Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices," (in eng), ArXiv, Feb 9 2022.
  109. L. Mocerino and A. Calimera, "Fast and Accurate Inference on Microcontrollers With Boosted Cooperative Convolutional Neural Networks (BC-Net)," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 1, pp. 77-88, 2021, doi: 10.1109/TCSI.2020.3039116.
  110. e. a. M. Courbariaux, "“Binarized neural networks: training deep neural networks with weights and activations constrained to +1 or -1,," http://arxiv.org/abs/1602.02830, ” 2016,.
  111. e. a. B. McDanel, "“Embedded binarized neural networks,”" http://arxiv.org/abs/1709 .02260., 2017,.
  112. S. Anwar, K. Hwang, and W. Sung, "Structured Pruning of Deep Convolutional Neural Networks," J. Emerg. Technol. Comput. Syst., vol. 13, no. 3, p. Article 32, 2017, doi: 10.1145/3005348.
  113. e. a. - I. Fedorov, "“Sparse: sparse architecture search for CNNs on resourceconstrained microcontrollers,," ” Advances in Neural Information Processing Systems,, vol. vol. 32, , 2019.
  114. “. "TensorFlow model optimization,”." Google, . (accessed.
  115. J. Yu, A. Lukefahr, D. Palframan, G. Dasika, R. Das, and S. Mahlke, "Scalpel: Customizing dnn pruning to the underlying hardware parallelism," ACM SIGARCH Computer Architecture News, vol. 45, no. 2, pp. 548-560, 2017.
  116. H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf, "Pruning filters for efficient convnets," arXiv preprint arXiv:1608.08710, 2016.
  117. J. Yu, A. Lukefahr, D. Palframan, G. Dasika, R. Das, and S. Mahlke, "Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism," presented at the Proceedings of the 44th Annual International Symposium on Computer Architecture, Toronto, ON, Canada, 2017. [Online]. Available: https://doi.org/10.1145/3079856.3080215.
  118. T. Z. S. Ye, K. Zhang et al. , "“A unified framework of DNN weight pruning and weight clustering/quantization using ADMM,”" http://arxiv.org/abs/1811.01907, , 2018,.
  119. L. Meng and N. Suda ", “Optimizing Power and Performance for Machine Learning at the Edge: Model Deployment Overview,”" ARM AI - AI Virtual Tech Talks Series, pp. pp. 1–35,, 2020.
  120. N. Schizas, A. Karras, C. Karras, and S. Sioutas, "TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review," Future Internet, vol. 14, no. 12, p. 363, 2022. [Online]. Available: https://www.mdpi.com/1999-5903/14/12/363.
  121. Hari Kishan Kondaveeti et al, in Advancement in Business Analytics Tools for Higher Financial Performance book,, -,2023, ch. Lightweight Deep Learning: Introduction, Advancements, and Applications capter,.
  122. e. a. Martín Abadi "Tensorflow: A system for large-scale machine learning," In OSDI, , 2016.
  123. e. a. Han Cai, " Once for All: Train One Network
  124. and Specialize it for Efficient Deployment," In ICLR,, 2020.
  125. e. a. Han Cai, " ProxylessNAS: Direct Neural Architecture Search on Target Task
  126. and Hardware," In ICLR,, 2019.
  127. e. a. f. Tianqi Chen, "An automated end-to-end optimizing compiler for deep learning," In OSDI,, 2018.
  128. e. a. Jungwook Choi, " Pact: Parameterized clipping activation for quantized neural networks," arXiv preprint arXiv:1805.06085,, 2018.
  129. Matthieu Courbariaux and Yoshua Bengio., "Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1," . arXiv preprint arXiv:1602.02830, 2016.
  130. e. a. Yunchao Gong, "Compressing deep convolutional networks using vector quantization.," arXiv preprint arXiv:1412.6115, , 2014.
  131. e. a. Song Han, "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.," In ICLR,, 2016.
  132. e. a. Song Han, ". Learning both Weights and Connections for Efficient Neural Networks.," In NeurIPS,, 2015.
  133. e. a. Kaiming He, "Deep Residual Learning for Image Recognition.," In CVPR,, 2016.
  134. e. a. Yihui He, "AMC: AutoML for Model Compression and Acceleration on Mobile Devices," In ECCV, , 2018.
  135. Y. H. a. . ". Channel pruning for accelerating very deep neural networks," In ICCV,, 2017.
  136. Zhang X, et al. ,, "Shufflenet: an Extremely Efficient Convolutional
  137. Neural Network for Mobile Devices[J]," 2017.
  138. Y.-D. K. al, "Compression
  139. of deep convolutional neural networks for fast and low power mobile applications,". arXiv preprint arXiv:1511.06530,, 2015.
  140. e. a. Liangzhen Lai, " Cmsis-nn: Efficient neural network kernels for arm
  141. cortex-m cpus," arXiv preprint arXiv:1801.06601,, 2018.
  142. T. Lawrence and L. Zhang, "IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices," Sensors, vol. 19, no. 24, p. 5541, 2019. [Online]. Available: https://www.mdpi.com/1424-8220/19/24/5541.
  143. e. a. Vadim Lebedev, " Speeding-up convolutional neural networks using fine-tuned cp-decomposition.," arXiv preprint arXiv:1412.6553,, 2014.
  144. E. L. a. N. D. Lane.. " Neural networks on microcontrollers: saving memory at inference via operator reordering," arXiv preprint arXiv:1910.05110,, 2019.
  145. e. a. Ji Lin, " Runtime neural pruning.," In NeurIPS, , 2017.
  146. e. a. - Haoxiao Liu, "DARTS: Differentiable Architecture Search.," In ICLR,, 2019.
  147. e. a. Zechun Liu, "MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning," In ICCV,, . 2019.
  148. e. a. Ningning Ma, "ShuffleNet V2: Practical Guidelines for
  149. Efficient CNN Architecture Design.," in In ECCV, 2018.
  150. e. a. Ilija Radosavovic, " Designing network design spaces," in arXiv preprint arXiv:2003.13678,, 2020.
  151. M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, "XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks," in Computer Vision – ECCV 2016, Cham, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., 2016// 2016: Springer International Publishing, pp. 525-542.
  152. e. a. Manuele Rusci, "Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollers," In MLSys, , 2020.
  153. e. a. Mark Sandler, " MobileNetV2: Inverted Residuals and Linear Bottlenecks.," in In CVPR, 2018.
  154. e. a. Mingxing Tan, " MnasNet: Platform-Aware Neural Architecture Search for Mobile," in In CVPR,, .. 2019.
  155. e. a. H. KuanWang, "Hardware-Aware Automated Quantization with Mixed Precision," in In CVPR, 2019.
  156. e. a. Bichen Wu, " FBNet: Hardware-Aware Efficient ConvNet Design via
  157. Differentiable Neural Architecture Search," in In CVPR,, . 2019.
  158. e. a. Xiangyu Zhang, "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices.," in In CVPR,, 2018.
  159. e. a. - Shuchang Zhou, "Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients," arXiv preprint arXiv:1606.06160,, . 2016.
  160. e. a. Chenzhuo Zhu, "Trained ternary quantization.," arXiv preprint arXiv:1612.01064,, 2016.
  161. B. Z. a. Q. V. Le., "Neural Architecture Search with Reinforcement Learning.," In ICLR,, 2017.
  162. e. a. Barret Zoph, "Learning Transferable Architectures for Scalable Image Recognition.," in In CVPR, , 2018.
  163. Y. Li, Z. Li, T. Zhang, P. Zhou, S. Feng, and K. Yin, "Design of a Novel Neural Network Compression Method for Tiny Machine Learning," presented at the Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, Xiamen, China, 2022. [Online]. Available: https://doi.org/10.1145/3501409.3501526.
  164. W. L. e. al., "A review of deep neural network model
  165. compression techniques for embedded applications[J]," Journal of Beijing
  166. Jiaotong University,, 2017.
  167. L. Jin, W. Yang, S. Wang, Z. Cui, X. Chen, and L. Chen, "A hybrid pruning method for convolutional neural network compression," Minicomput. Syst, vol. 39, pp. 2596-2601, 2018.
  168. Su Loach.. , " Research and application of lightweight target detection algorithm
  169. based on deep learning[D]," South China University of Technology, 2020.
  170. M. Dehghanian and M. S. M. Mosadegh, "Ternary Weighted Function and Beurling Ternary Banach Algebra l 1 w (S)," in Abstract & Applied Analysis, 2011.
  171. D. J., "Research on model compression and forward acceleration techniques for embedded deep neural networks [D]." University of Science and Technology of China,, 2018.
  172. e. a. Iandola FN, "Squeezenet: Alexnet-level Accuracy
  173. with 50x Fewer Parameters and <0.5mb Model Size[J],", 2016.
  174. Chollet F., " Xception: Deep Learning with Depthwise Separable Convolutions[C]// " in {ieee} Conference on Computer Vision and Pattern Recognition ( {cvpr}), 2017: : Institute of Electrical and Electronics Engineers Inc. .
  175. S. L. . "Research and application of lightweight target detection algorithm based on deep learning[D]," South China University of Technology,, 2020.
  176. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," presented at the Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, 2015. [Online]. Available: https://proceedings.mlr.press/v37/ioffe15.html.
  177. M. S. Diab and E. Rodriguez-Villegas, "Embedded machine learning using microcontrollers in wearable and ambulatory systems for health and care applications: A review," IEEE Access, vol. 10, pp. 98450-98474, 2022.
  178. P. P. Ray, "A review on TinyML: State-of-the-art and prospects," Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 4, pp. 1595-1623, 2022.
  179. V. Tsoukas, E. Boumpa, G. Giannakas, and A. Kakarountas, "A review of machine learning and tinyml in healthcare," in Proceedings of the 25th Pan-Hellenic Conference on Informatics, 2021, pp. 69-73.
  180. C. Nicolas, B. Naila, and R.-C. Amar, "Tinyml smart sensor for energy saving in internet of things precision agriculture platform," in 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), 2022: IEEE, pp. 256-259.
  181. Y. Abadade, A. Temouden, H. Bamoumen, N. Benamar, Y. Chtouki, and A. S. Hafid, "A comprehensive survey on tinyml," IEEE Access, 2023.
  182. N. Nasrullah, J. Sang, M. S. Alam, M. Mateen, B. Cai, and H. Hu, "Automated lung nodule detection and classification using deep learning combined with multiple strategies," Sensors, vol. 19, no. 17, p. 3722, 2019.
  183. S. S. Sanagala, S. K. Gupta, V. K. Koppula, and M. Agarwal, "A fast and light weight deep convolution neural network model for cancer disease identification in human lung (s)," in 2019 18th IEEE international conference on machine learning and applications (ICMLA), 2019: IEEE, pp. 1382-1387.
  184. F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers, and D. Pfeiffer, "Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization," Scientific reports, vol. 9, no. 1, p. 6268, 2019.
  185. S. Rajaraman, J. Siegelman, P. O. Alderson, L. S. Folio, L. R. Folio, and S. K. Antani, "Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-rays," Ieee Access, vol. 8, pp. 115041-115050, 2020.
  186. S. B. Shuvo, S. N. Ali, S. I. Swapnil, T. Hasan, and M. I. H. Bhuiyan, "A lightweight cnn model for detecting respiratory diseases from lung auscultation sounds using emd-cwt-based hybrid scalogram," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 7, pp. 2595-2603, 2020.
  187. P. Sumari, S. J. Syed, and L. Abualigah, "A novel deep learning pipeline architecture based on CNN to detect Covid-19 in chest X-ray images," Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 6, pp. 2001-2011, 2021.
  188. M. F. M. Gouher, K. Ramanjaneyulu, C. M. Bhuma, M. B. Mohammad, and U. Lekhana, "A LIGHTWEIGHT CNN FOR LUNG NODULE DETECTION AND CLASSIFICATION FROM CHEST RADIOGRAPHS."
  189. V. V. K. Shukla, M. Tanmisha, R. Aluru, B. Nagisetti, and P. Tumuluru, "Lung nodule detection through ct scan images and dnn models," in 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021: IEEE, pp. 962-967.
  190. A. Kumar, A. Shar