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Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks


With an intention of improving healthcare performance, wearable technology products utilize several digital health sensors which are classically linked into sensor networks, including body-worn and ambient sensors. On the other hand, intracerebral hemorrhage (ICH) defines the injury of blood vessels in the brain regions, which is accountable for 10–15% of strokes. X-ray computed tomography (CT) scans are commonly employed to determine the position and size of the hemorrhages. Manual segmentation of the CT scans by planimetry using a radiologist is effective; however, it consumes more time. Therefore, this paper develops deep learning (DL)–based ICH diagnosis using GrabCut-based segmentation with synergic deep learning (SDL), named GC-SDL model. The proposed method make use of Gabor filtering for noise removal, thereby the image quality can be raised. In addition, GrabCut-based segmentation technique is applied to identify the diseased portions effectively in the image. To perform the feature extraction process, SDL model is utilized and finally, softmax (SM) layer is employed as a classifier. In order to investigate the performance of the GC-SDL model, an extensive set of experimentation takes place using a benchmark ICH dataset, and the results are examined under different evaluation metrics. The experimental outcome stated that the GC-SDL model has reached a higher sensitivity of 94.01%, specificity of 97.78%, precision of 95.79%, and accuracy of 95.73%.

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  1. Wang C, Lai W (2019) A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry. Personal and Ubiquitous Computing, pp1–9

  2. Cola, G. and Vecchio, A., 2018. Wearable systems for e-health and wellbeing. personal and ubiquitous computing, (2018) 22:225

  3. Divani AA, Majidi S, Luo X, Souslian FG, Zhang J, Abosch A, Tummala RP (2011) The ABCs of accurate volumetric measurement of cerebral hematoma. Stroke 42(6):1569–1574

    Article  Google Scholar 

  4. Wang S, Lou M, Liu T, Cui D, Chen X, Wang Y (2013) Hematoma volume measurement in gradient echo MRI using quantitative susceptibility mapping. Stroke 44 (8), 2315–2317 (Aug. 1)

  5. Kang S, Paul A, Jeon G (2017) Reduction of mixed noise from wearable sensors in human-motion estimation. Comput Electr Eng 61:287–296

    Article  Google Scholar 

  6. Ma L, Wu J, Zhang J, Wu Z, Jeon G, Zhang Y (Sept 2020) Research on sea clutter reflectivity using deep learning model in industry 4.0. IEEE Trans Industrial Informatics 16(9):5929–5937

    Article  Google Scholar 

  7. Lakshmanaprabu SK, Mohanty SN, Shankar K, Arunkumar N, Ramireze G (2019) Optimal deep learning model for classification of lung cancer on CT images. Futur Gener Comput Syst 92:374–382

    Article  Google Scholar 

  8. Sikkandar, M. Y., Alrasheadi, B. A., Prakash, N. B., Hemalakshmi, G. R., Mohanarathinam, A., & Shankar, K. (2020). Deep learning based an automated skin lesion segmentation and intelligent classification model. Journal of ambient intelligence and humanized computing, 1-11

  9. Mohamed Elhoseny, Gui-Bin Bian, SK. Lakshmanaprabu, K. Shankar, Amit Kumar Singh, Wanqing Wu, “Effective features to classify ovarian cancer data in internet of medical things”, Computer Networks, Volume 159, Pages 147–156, August 2019

  10. Ahmed I, Din S, Jeon G, Piccialli F (July 2020) Exploring deep learning models for overhead view multiple object detection. IEEE Internet Things J 7(7):5737–5744

    Article  Google Scholar 

  11. Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285:923–931

    Article  Google Scholar 

  12. Grewal, M.; Srivastava, M.M.; Kumar, P.; Varadarajan, S. RADnet: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 4–7 April 2018; pp. 281–284

  13. Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Y, Wang X, Bai J, Cao K, Song Q, Zhang H, Chen W, Guo X, Xia J (2019) Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 29:6191–6201

    Article  Google Scholar 

  14. Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, Ebert SA, Pomerantz SR, Romero JM, Kamalian S, Gonzalez RG, Lev MH, Do S (2019) An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 3:173–182

    Article  Google Scholar 

  15. Chang P, Kuoy E, Grinband J, Weinberg B, Thompson M, Homo R, Chen J, Abcede H, Shafie M, Sugrue L et al (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol 39:1609–1616

    Article  Google Scholar 

  16. Jnawali, K.; Arbabshirani, M.R.; Rao, N.; Patel, A.A. Deep 3D convolution neural network for CT brain hemorrhage classification. In Medical Imaging 2018: Computer-Aided Diagnosis; International Society for Optics and Photonics: Washington, DC, USA, 2018; Volume 10575, p. 105751C

  17. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ (2018) Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med 1:9

    Article  Google Scholar 

  18. Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, Chen J, Abcede H, Shafie M, Sugrue L, Filippi CG (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol 39(9):1609–1616

    Article  Google Scholar 

  19. Majumdar, A., Brattain, L., Telfer, B., Farris, C. and Scalera, J., 2018, July. Detecting intracranial hemorrhage with deep learning. In 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 583-587). IEEE

  20. K. Shankar, Abdul Rahaman WahabSait, DeepakGupta, S.K.Lakshmanaprabu, Ashish Khanna, Hari Mohan Pandey, “Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model”, Pattern Recognition Letters, Volume 133, Pages 210–216, May 2020


  22. Hssayeni, M.D., Croock, M.S., Salman, A.D., Al-khafaji, H.F., Yahya, Z.A. and Ghoraani, B., 2020. Intracranial hemorrhage segmentation using a deep convolutional model. Data, 5(1), p.14

  23. Davis, V. and Devane, S., 2017, December. Diagnosis & classification of brain hemorrhage. In 2017 international conference on advances in computing, communication and control (ICAC3) (pp. 1-6). IEEE

  24. Danilov G, Kotik K, Negreeva A, Tsukanova T, Shifrin M, Zakharova N, Batalov A, Pronin I, Potapov A (2020) Classification of intracranial hemorrhage subtypes using deep learning on CT scans. Studies in Health Technology and Informatics 272:370–373

    Google Scholar 

  25. Karki M, Cho J, Lee E, Hahm MH, Yoon SY, Kim M, Ahn JY, Son J, Park SH, Kim KH, Park S (2020) CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings. Artificial Intelligence in Medicine, p 101850

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Dr. K. Shankar sincerely acknowledge the financial support of RUSA–Phase 2.0 grant sanctioned vide Letter No. F. 24-51/2014-U, Policy (TNMulti-Gen), Dept. of Edn. Govt. of India, Dt. 09.10.2018.

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Anupama, C.S.S., Sivaram, M., Lydia, E.L. et al. Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Pers Ubiquit Comput 26, 1–10 (2022).

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  • Wearable sensors
  • Medical imaging
  • Deep learning
  • Segmentation
  • ICH
  • Classification