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Computer-aided automatic transfer learning based approach for analysing the effect of high-frequency EMF radiation on brain

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Abstract

An electromagnetic field radiations (EMF) emanating from the cell phones affect the brain and other organs in living organisms. Therefore, the objective of the present study is to examine whether the EMF radiations affect the brain cells or not, using the transfer learning-based methodology. The observations made in the present study are based on our experiment with the Drosophila melanogaster. The microscopic brain-images of drosophila, (should be read as micro-images) exposed to and not exposed to the high-frequency EMF radiations were acquired for the analysis purposed. The comprehensive set of features were extracted from the micro-images in EMF-exposed and Non-exposed class drosophila using the pre-trained convolution neural networks (CNNs). Further, the support Vector Machine (SVM) has been used to find an optimal hyperplane in higher dimensional feature space which separates the feature representations of the micro-images from both the classes. The % accuracy of SVM in classifying the features extracted from the micro-images from both the classes using the pre-trained VGG19 network is 87.3% for the 5-fold cross-validation. The discrimination in feature sets extracted from the micro-images signifies that the EMF radiations have affected the drosophila brain cells. Experimental results reveal that the prolonged exposure to EMF radiations might have affected the drosophila brain which approves the assumed hypothesis.

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References

  1. Adebayo EA, Adeeyo AO, Ogundiran MA, Olabisi O (2018) Bio-physical effects of radiofrequency electromagnetic radiation (RF-EMR) on blood parameters, spermatozoa, liver, kidney and heart of albino rats. J King Saud Univ Sci 31(4):813–821

  2. Banik PP, Saha R, Kim K (2020) An automatic nucleus segmentation and CNN model-based classification method of white blood cell. Expert Syst Appl 149:113211. https://doi.org/10.1016/j.eswa.2020.113211

    Article  Google Scholar 

  3. Borjali A, Chen AF, Muratoglu OK, Morid MA, Varadarajan KM (2020) Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J Orthop Res 38(7):1465–1471. https://doi.org/10.1002/jor.24617

    Article  Google Scholar 

  4. Chowdhary CL, Acharjya DP (2017) Clustering algorithm in Possibilistic exponential fuzzy C-mean segmenting medical images. J Biomimetics Biomater Biomed Eng 30:12–23. https://doi.org/10.4028/www.scientific.net/jbbbe.30.12

    Article  Google Scholar 

  5. Chowdhary CL, Acharjya DP (2018) Segmentation of mammograms using a novel intuitionistic Possibilistic fuzzy C-mean clustering algorithm. In: Panigrahi B, Hoda M, Sharma V, Goel S (eds) Nature inspired computing. Advances in intelligent systems and computing, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-6747-1_9

    Chapter  Google Scholar 

  6. Dawud AM, Yurtkan K, Oztoprak H (2019) Application of deep learning in neuroradiology: brain haemorrhage classification using transfer learning. Comput Intell Neurosci 2019:4629859, 12 pages. https://doi.org/10.1155/2019/4629859

    Article  Google Scholar 

  7. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Miami, pp 248–255

  8. Dhungel N, Carneiro G, Bradley AP (2015) Deep learning and structured prediction for the segmentation of mass in mammograms. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp 605–612

  9. Gao Z, Wang L, Member S, Zhou L, Member S, Zhang J (2016) HEp-2 cell image classification with deep convolutional neural networks. IEEE J Biomed Health Inf 21(2):416–428. https://doi.org/10.1109/JBHI.2016.2526603

    Article  Google Scholar 

  10. Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25–32. https://doi.org/10.1016/j.jbi.2018.08.006

    Article  Google Scholar 

  11. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90.

  12. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 2261–2269. https://doi.org/10.1109/CVPR.2017.243.

  13. Iqbal S, Ghani MU, Saba T, Rehman A (2018) Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc Res Tech 81(4):419–427. https://doi.org/10.1002/jemt.22994

    Article  Google Scholar 

  14. Jeong Y et al (2016) 1950 MHz electromagnetic fields ameliorate Aβ pathology in Alzheimer’s disease mice. Curr Alzheimer Res 12(5):481–492

    Article  Google Scholar 

  15. Khan S, Islam N, Jan Z, Ud I, Rodrigues JJPC (2019) A novel deep learning-based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn Lett 125:1–6. https://doi.org/10.1016/j.patrec.2019.03.022

    Article  Google Scholar 

  16. Khurana VG, Teo C, Kundi M, Hardell L, Carlberg M (2009) Cell phones and brain tumors: a review including the long-term epidemiologic data. Surg Neurol 72(3):205–214

    Article  Google Scholar 

  17. Kim JH, Lee JK, Kim HG, Kim KB, Kim HR (2019) Possible effects of radiofrequency electromagnetic field exposure on central nerve system. Biomol Ther 27(3):265–275

    Article  Google Scholar 

  18. Kishore K, Venkateshu KV, Sridevi NS (2019) Effect of 1800–2100 MHz electromagnetic radiation on learning-memory and hippocampal morphology in Swiss albino mice. J Clin Diagn Res 13(2):14–17

  19. Liu TYA, Ting DSW, Yi PH (2020) Deep learning and transfer learning for optic disc laterality detection: implications for machine learning in Neuro-ophthalmology. J Neuroophthalmol 40(2):178–184. https://doi.org/10.1097/WNO.0000000000000827

    Article  Google Scholar 

  20. Lopes UK, Valiati JF (2017) Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput Biol Med 89(1):135–143. https://doi.org/10.1016/j.compbiomed.2017.08.001

    Article  Google Scholar 

  21. Mausset-Bonnefont AL, Hirbec H, Bonnefont X, Privat A, Vignon J, De Sèze R (2004) Acute exposure to GSM 900-MHz electromagnetic fields induces glial reactivity and biochemical modifications in the rat brain. Neurobiol Dis 17(3):445–454

    Article  Google Scholar 

  22. Morgan LL, Miller AB, Sasco A, Davis DL (2015) Mobile phone radiation causes brain tumors and should be classified as a probable human carcinogen (2A) (review). Int J Oncol 46(5):1865–1871

    Article  Google Scholar 

  23. Okatan DO, Okatan AE, Hancı H, Demir S, Yaman SO, Odacı SCE (2018) Effects of 900-MHz electromagnetic fields exposure throughout middle/late adolescence on the kidney morphology and biochemistry of the female rat. Toxicol Ind Health 34(10):693–702

    Article  Google Scholar 

  24. Ouadah NS, Lecomte A, Robidel F, Olsson A, Deltour I, Schüz J, Blazy K, Villégier AS (2018) Possible effects of radiofrequency electromagnetic fields on in vivo C6 brain tumors in Wistar rats. J Neuro-Oncol 140(3):539–546. https://doi.org/10.1007/s11060-018-03012-y

    Article  Google Scholar 

  25. Pandey UB, Nichols CD (2011) Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery. Pharmacol Rev 63(2):411–436. https://doi.org/10.1124/pr.110.003293

  26. Razavinasab M, Moazzami K, Shabani M (2014) Maternal mobile phone exposure alters intrinsic electrophysiological properties of CA1 pyramidal neurons in rat offspring. Toxicol Ind Health 32(6):968–979

    Article  Google Scholar 

  27. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556

  28. Singh A, Singh N, Jindal T, Dutta MK (2020) A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning. Biomed Signal Process Control 57. https://doi.org/10.1016/j.bspc.2019.101821

  29. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York

    Book  Google Scholar 

  30. Wyde ME, Horn TL, Capstick MH, Ladbury JM, Koepke G, Wilson PF, Kissling GE, Stout MD, Kuster N, Melnick RL, Gauger J, Bucher JR, McCormick DL (2018) Effect of cell phone radiofrequency radiation on body temperature in rodents: pilot studies of the National Toxicology Program's reverberation chamber exposure system. Bioelectromagnetics 39(3):190–199. https://doi.org/10.1002/bem.22116

    Article  Google Scholar 

  31. Xue Y, Bigras G, Hugh J, Ray N (2019) Training convolutional neural networks and compressed sensing end-to-end for microscopy cell detection. IEEE Trans Med Imaging 38(11):2632–2641. https://doi.org/10.1109/TMI.2019.2907093

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University New Campus, Lucknow, 226031, India

    Ritesh Maurya & Malay Kishore Dutta

  2. Amity Institute for Environmental Toxilogical, Safety and Management, Noida, India

    Neha Singh & Tanu Jindal

  3. Dr. A.P.J. Abdul Kalam Technical University New Campus, Lucknow, 226031, India

    Vinay Kumar Pathak

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  1. Ritesh Maurya
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  2. Neha Singh
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  5. Malay Kishore Dutta
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Correspondence to Malay Kishore Dutta.

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The authors declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.

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In our knowledge, as per the Indian laws, working with Drosophila melanogaster, does not requires ethical clearance.

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Maurya, R., Singh, N., Jindal, T. et al. Computer-aided automatic transfer learning based approach for analysing the effect of high-frequency EMF radiation on brain. Multimed Tools Appl 81, 13713–13729 (2022). https://doi.org/10.1007/s11042-020-10204-0

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  • DOI: https://doi.org/10.1007/s11042-020-10204-0

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