Skip to main content

RADIoT: The Unifying Framework for IoT, Radiomics and Deep Learning Modeling

Part of the Intelligent Systems Reference Library book series (ISRL,volume 209)

Abstract

The IoT revolution reshapes contemporary healthcare systems by incorporate economic, social, and technological prospects. It is progressing from conventional healthcare systems to more personalized healthcare systems, where patients can be monitored, diagnosed and treated effortlessly. Radiomics is a sub-field of machine learning (ML) that mines quantitative features from radiological images relying on an image-based approach with ML models, which procure information surpassing orthodox medical imaging analysis as diagnosis, prognosis, prediction and response to therapy. The upsurge in the number of radiological images increases the workload of radiologist which in turns decreases their performance, thus they can only detect and evaluate a small portion of information present in images within a short-time. Hence, there is need for a better method for the increase in radiological image selection, detection and evaluation processes thereby reducing the workload of experts. Therefore, this chapter discusses the different types and sources of radiological data, feature extraction and selection method for image analysis. The chapter also presents different ML models ideal for the radiomics and parameter tuning. The challenges, applicability and limitations of Radiomics are also described in this chapter. The radiomic process involves radiological image gathering, segmentation, feature extraction and selection, model building and evaluation. Each of the stage of the process workflow is carefully evaluated for development of a reliable, effective and robust model to be shifted into medical practice for disease diagnosis and prognosis response to treatment.

Keywords

  • Radiomics
  • Internet of Things
  • Deep learning
  • Image segmentation
  • Texture descriptors
  • Quantitative imaging

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-16-2972-3_6
  • Chapter length: 20 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-981-16-2972-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillion-Robin, J.-C., Pieper, S., & Aerts, H. J. W. L. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104–e107.

    CrossRef  Google Scholar 

  2. Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., & Aerts, H. J. W. L. (2012). Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer, 48, 441–446.

    CrossRef  Google Scholar 

  3. Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563–577.

    CrossRef  Google Scholar 

  4. Akmandor, O. A., & Jha, N. K. (2017). Smart health care: An edge-side computing perspective. IEEE Consumer Electronics Magazine, 7(1), 29–37.

    CrossRef  Google Scholar 

  5. Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020). Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognition Letters, 135, 346–353.

    CrossRef  Google Scholar 

  6. Chen, P.-H., & Cross, N. (2018). IoT in radiology: Using Raspberry Pi to automatically log telephone calls in the reading room. Journal of Digital Imaging, 31, 371–378.

    CrossRef  Google Scholar 

  7. Gil, D., Ferrández, A., Mora-Mora, H., & Peral, J. (2016). Internet of Things: A review of surveys based on context aware intelligent services. Sensors, 16.

    Google Scholar 

  8. Upton, E. (2016). Ten Millionth raspberry pi, and a new kit. Raspberry Pi. [Online].

    Google Scholar 

  9. Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for internet of things data analysis: A survey. Digital Communications and Networks, 4, 161–175.

    CrossRef  Google Scholar 

  10. Qin, Y., Sheng, Q. Z., Falkner, N. J., Dustdar, S., Wang, H., & Vasilakos, A. V. (2016). When things matter: A survey on data-centric internet of things. Journal of Network and Computer Applications, 64, 137–153.

    CrossRef  Google Scholar 

  11. Sheng, Z., Yang, S., Yu, Y., Vasilakos, A. V., McCann, J. A., & Leung, K. K. (2013) A survey on the IETF protocol suite for the Internet of Things: Standards, challenges and opportunities. IEEE Wireless Communications, 20(6), 91–98.

    Google Scholar 

  12. Yadav, A., Kumar Singh, V., Kumar Bhoi, A., Marques, G., Garcia-Zapirain, B., & de la Torre Díez, I. (2020). Wireless body area networks: UWB wearable textile antenna for telemedicine and mobile health systems. Micromachines, 11(6), 558.

    CrossRef  Google Scholar 

  13. Marques, G., Bhoi, A. K., de Albuquerque, V. H. C., K.S., H. (Eds.), (2021). IoT in healthcare and ambient assisted living. Springer.

    Google Scholar 

  14. Marques, G., Miranda, N., Kumar Bhoi, A., Garcia-Zapirain, B., Hamrioui, S., & de la Torre Díez, I. (2020). Internet of Things and enhanced living environments: Measuring and mapping air quality using cyber-physical systems and mobile computing technologies. Sensors, 20(3), 720.

    CrossRef  Google Scholar 

  15. Oniani, S., Marques, G., Barnovi, S., Pires, I. M., & Bhoi, A. K. (2020). Artificial intelligence for internet of things and enhanced medical systems. In Bio-inspired neurocomputing (pp. 43–59). Springer.

    Google Scholar 

  16. Chandy, A. (2019). A review on IoT based medical imaging technology for healthcare applications. Journal of Innovative Image Processing (JIIP), 1(1), 51–60.

    CrossRef  Google Scholar 

  17. Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., van Wijk, Y., Woodruff, H., van Soest, J., Lustberg, T., Roelofs, E., van Elmpt, W., Dekker, A., Mottaghy, F. M., Wildberger, J. E., & Walsh, S. (2017) Radiomics: The bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 17, 749–762.

    Google Scholar 

  18. Bizzego, A., Bussola, N., Salvalai, D., Chierici, M., Maggio, V., Jurmany, G., & Furlanello, C. (2016) Integrating deep and radiomics features in cancer bioimaging.

    Google Scholar 

  19. Court, L. E., Fave, X., Mackin, D., Lee, J., Yang, J., & Zhang, L. (2016). Computational resources for radiomics. Translational Cancer Research, 5, 340–348.

    CrossRef  Google Scholar 

  20. Balagurunathan, Y., Gu, Y., Wang, H., Kumar, V., Grove, O., Hawkins, S., Kim, J., Goldgof, D. B., Hall, L. O., Gatenby, R. A., & Gillies, R. J. (2014). Reproducibility and prognosis of quantitative features extracted from CT images. Translational Oncology, 7(1), 72–87.

    CrossRef  Google Scholar 

  21. Hui, G., & Oksam, C. (2010). Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recognition, 43(7), 2406–2417.

    CrossRef  Google Scholar 

  22. Ye, X., Beddoe, G., & Slabaugh, G. (2010). Automatic graph cut segmentation of lesions in CT using mean shift superpixels. International Journal of Biomedical Imaging, 983963.

    Google Scholar 

  23. Chen, X., Udupa, J. K., Bagci, U., Zhuge, Y., & Yao, J. (2012). Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Transactions on Image Processing, 21(4), 2035–2046.

    MathSciNet  CrossRef  Google Scholar 

  24. Suzuki, K., Kohlbrenner, R., Epstein, M. L., Obajuluwa, A. M., Xu, J., & Hori, M. (2010). Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. Medical Physics, 37(5), 2159–2166.

    CrossRef  Google Scholar 

  25. Zhou, M., Scott, J., Chaudhury, B., Hall, J., Goldgof, D., Yeom, K. W., Ou, I. M. Y., Kalpathy-Cramer, J., Napel, S., Gillies, R., Gevaert, O., & Gatenby, R. (2018). Radiomics in brain tumor: Image assessment, quantitative feature descriptors, and machine-learning approaches. American Journal of Neuroradiology, 39(2), 208–216.

    Google Scholar 

  26. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ‘05), San Diego, Calif, USA, June 2005.

    Google Scholar 

  27. Ojala, T., Pietikäinen, M., & Harwood, D. (1994). Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994).

    Google Scholar 

  28. Nanni, I., Lumini, A., & Brahnam, S. (2010). Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine, 49(2), 117–125.

    CrossRef  Google Scholar 

  29. Khoshgoftaar, T., Dittman, D., Wald, R., & Fazelpour, A. (2013). First order statistics based feature selection: A diverse and powerful family of feature seleciton techniques. In Proceedings of 11th International Conference on Machine Learning and Applications, Boca Raton, FL, Boca Raton, Florida.

    Google Scholar 

  30. Rivera, A. R., Castillo, J. R., & Chae, O. O. (2013). Local directional number pattern for face analysis: Face and expression recognition. IEEE Transactions on Image Processing, 22(5), 1740–1752.

    MathSciNet  CrossRef  Google Scholar 

  31. Song, T., Li, H., Meng, F., Wu, Q., & Cai, J. (2018). LETRIST: Locally encoded transform feature histogram for rotation-invariant texture classification. IEEE Transactions on Circuits and Systems for Video Technology, 28(7), 1565–1579.

    CrossRef  Google Scholar 

  32. Kannala, J., & Rahtu, E. (2012). BSIF: Binarized statistical image features. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba.

    Google Scholar 

  33. Belahcene, M., Laid, M., Chouchane, A., Ouamane, A., & Bourennane, S. (2016). Local descriptors and tensor local preserving projections in face recognition. In Proceedings of the 6th European Workshop at the Visual Information Processing (EUVIP), Marseille, France.

    Google Scholar 

  34. Lillholm, M., & Griffin, L. (2008). Novel image feature alphabets for object recognition. In 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Florida, USA.

    Google Scholar 

  35. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR).

    Google Scholar 

  36. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV.

    Google Scholar 

  37. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV.

    Google Scholar 

  38. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT.

    Google Scholar 

  39. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceeding IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI.

    Google Scholar 

  40. Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019), Long Beach, California.

    Google Scholar 

  41. Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. In Proceedings IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT.

    Google Scholar 

  42. Huynh, B., Li, H., & Giger, M. L. (2016). Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. Journal of Medical Imaging (Bellingham), 2(3), 034501.

    CrossRef  Google Scholar 

  43. Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417.

    CrossRef  Google Scholar 

  44. Gil, D., Díaz-Chito, K., Sánchez, C., & Hernández-Sabaté, A. (2020). Early screening of SARS-CoV-2 by intelligent analysis of X-ray images. arXiv preprint arXiv:2005.13928.

  45. Motwani, M., Dey, D., Berman, D. S., Germano, G., Achenbach. S., Al-Mallah. M. H., Chang, H. J., et al. (2017). Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: A 5-year multicentre prospective registry analysis. European Heart Journal, 38(7), 500–507.

    Google Scholar 

  46. Agrawal, R. K., Kaur, B., & Sharma, S. (2020). Quantum based whale optimization al-gorithm for wrapper feature selection. Applied Soft Computing, 89(106092).

    Google Scholar 

  47. Wiharto, W., Suryani, E., & Cahyawati, V. (2019). The methods of duo output neural network ensemble for the prediction of coronary heart disease. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 7(1), 51–58.

    CrossRef  Google Scholar 

  48. Nilashi, M., Bin Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). An analytical method for diseases prediction using machine learning techniques. Computers and Chemical Engineering, 106, 212–223.

    Google Scholar 

  49. Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(4396), 1–28.

    Google Scholar 

  50. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Proceedings of the Annual Processing Systems, Long Beach, CA, USA.

    Google Scholar 

  51. Ordóñez, P. F., Cepeda, C. M., Garrido, J., & Chakravarty, S. (2017). Classification of images based on small local features: A case applied to microaneurysms in fundus retina images. Journal of Medical Imaging, 4(4), 041309.

    CrossRef  Google Scholar 

  52. Shafiee, M. J., Chung, A. G., Khalvati, F., Haider, M. A., & Wong, A. (2017). Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection. Journal of Medical Imaging, 4(4), 041305.

    CrossRef  Google Scholar 

  53. Vaidhya, K., Thirunavukkarasu, S., Alex, V., & Krishnamurthi, G. (2016). Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and TMultiple Sclerosis, Stroke and Traumatic Brain Injuries. (BrainLes 2015). Lecture notes in computer science.

    Google Scholar 

  54. Alex, K. V., Thirunavukkarasu, S., Kesavadas, C., & Krishnamurthi, G. (2017). Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation. Journal of Medical Imaging (Bellingham), 4(4), 041311.

    Google Scholar 

  55. Li, H., Giger, M. L., Huynh, B. Q., & Antropova, N. O. (2017). Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. Journal of Medical Imaging (Bellingham), 4(4), 041304.

    Google Scholar 

  56. Liu, S., Xie, Y., Jirapatnakul, A., & Reevesa, A. P. (2017). Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks. Journal of Medical Imaging (Bellingham), 4(4), 041308.

    Google Scholar 

  57. Shahedi, M., Cool, D. W., Bauman, G. S., Bastian-Jordan, M., Fenster, A., & Ward, A. D. (2017). Accuracy validation of an automated method for prostate segmentation in magnetic resonance imaging. Journal of Digit Imaging, 30, 782–795.

    CrossRef  Google Scholar 

  58. Cheng, R., Turkbey, B., Gandler. W., Agarwal, H. K., Shah, V. P., Bokinsky, A., McCreedy, E., Wang. S., Sankineni, S., Bernardo. M., Pohida. T., Choyke, P., & McAuliffe, M. J. (2014). Atlas based AAM and SVM model for fully automatic MRI prostate segmentation. In Conference Proceedings of IEEE Engineering Medical Biology Society (pp. 2881–2885).

    Google Scholar 

  59. Runkler, T. A. (2016). Data analytics: Models and algorithms for intelligent data analysis (2nd ed., p. 158). Springer Vieweg.

    Google Scholar 

  60. Folorunso, S. O., & Adeyemo, A. B. (2013). Alleviating classification problem of imbalanced dataset. African Journal of Computing and ICT, 6(1), 137–144.

    Google Scholar 

  61. Afshar, P., Mohammadi, A., Plataniotis, K. N., Oikonomou, A., & Benali, H. (2019). From handcrafted to deep-learning-based cancer radiomics: Challenges and opportunities. IEEE Signal Processing Magazine, 36, 132–160.

    CrossRef  Google Scholar 

  62. Kumar, S. M., & Majumder, D. (2018). Healthcare solution based on machine learning applications in IoT and edge computing. International Journal of Pure and Applied Mathematics, 119(16), 1473–1484.

    Google Scholar 

  63. Gillies, R., Kinahan, P., et al. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563–577.

    Google Scholar 

  64. Folorunso, S. O., & Adeyemo, A. B. (2012). Theoretical comparison of undersampling techniques against. In EIE’s 2nd International Conference on Computing, Energy, Networking, Robotics and Telecommunication (EIE 2012).

    Google Scholar 

  65. Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.

    CrossRef  Google Scholar 

  66. Wilson, D. L. (1972). Asymptotic properties of nearest neighbour rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, 2, 408–421.

    Google Scholar 

  67. Tomek, I. (1976). An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, 6(6), 448–452.

    Google Scholar 

  68. He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IEEE International Joint Conference Neural Networks, Hong Kong.

    Google Scholar 

  69. Vallières, M., Kay-Rivest, E., Perrin, L. J., Liem, X., Furstoss, C., Aerts, H. J. W. L., Khaouam, N., Nguyen-Tan, P. F., Wang, C. S., Sultanem, K., Seuntjens, J., & El Naqa, I. (2017). Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Scientific Reports, 7(1), 10117.

    Google Scholar 

  70. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.

    MathSciNet  MATH  Google Scholar 

  71. Vapnik, V. N. (1998). Adaptive and learning systems for signal processing communications, and control. In Statistical learning theory.

    Google Scholar 

  72. https://github.com/ieee8023/covid-chestxray-dataset. [Online].

  73. https://commons.wikimedia.org/wiki/Category:Magnetic_resonance_imaging#/media/File:Petmr.jpg

  74. https://en.wikipedia.org/wiki/X-ray_machine#/media/File:Projectional_radiography_components.jpg

  75. https://en.wikipedia.org/wiki/CT_scan#/media/File:UPMCEast_CTscan.jpg

  76. https://en.wikipedia.org/wiki/Positron_emission_tomography#/media/File:ECAT-Exact-HR--PET-Scanner.jpg

  77. https://en.wikipedia.org/wiki/Medical_ultrasound#/media/File:AlokaPhoto2006a.jpg

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sakinat Oluwabukonla Folorunso .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Folorunso, S.O., Awotunde, J.B., Ayo, F.E., Abdullah, KK.A. (2021). RADIoT: The Unifying Framework for IoT, Radiomics and Deep Learning Modeling. In: Kumar Bhoi, A., Mallick, P.K., Narayana Mohanty, M., Albuquerque, V.H.C.d. (eds) Hybrid Artificial Intelligence and IoT in Healthcare. Intelligent Systems Reference Library, vol 209. Springer, Singapore. https://doi.org/10.1007/978-981-16-2972-3_6

Download citation