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Link to original content: https://doi.org/10.1007/s11042-023-16676-0
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Automated schizophrenia detection model using blood sample scattergram images and local binary pattern

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Abstract

The main goal of this paper is to advance the field of automated Schizophrenia (SZ) detection methods by presenting a pioneering feature engineering technique that achieves high classification accuracy while maintaining low time complexity. Furthermore, we introduce a novel data type known as scattergram images, which can be obtained through a simple blood test. These scattergram images provide a cost-effective approach for SZ detection. The scattergram image datasets used in this research consist of images collected from 202 participants, with 106 individuals diagnosed with SZ and the remaining 96 individuals serving as control subjects. Our objective is to assess the ability of scattergram images to detect SZ. To achieve accurate classification with minimal computational burden, we propose a feature engineering model based on the local binary pattern (LBP) technique. Initially, a preprocessing method is applied to separate blood cells from the scattergram images, followed by image rotation to ensure robust results. Both 1D-LBP and 2D-LBP are utilized to extract informative features. Our feature engineering model incorporates iterative neighborhood component analysis (INCA) to select the most relevant features. In the classification phase, shallow classifiers are employed to demonstrate the capability of the extracted features for classification. Information fusion is accomplished using iterative hard majority voting (IHMV) to select the most accurate result. We have tested our proposal on the collected two scattergram image datasets and our proposal attained 89.29% and 90.58% classification accuracies on the used datasets, respectively. The findings of this study demonstrate the potential of scattergram images as an effective tool for SZ detection, thus serving as a promising new biomarker in the field. Our auto-detection model of SZ disease is clinically ready for use in hospital settings and outpatient clinics as an additional means to assist clinicians in their diagnostics procedure.

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Data availability

The data presented in this study are available on request from the corresponding author. The data is publicly available at https://www.kaggle.com/datasets/buraktaci/sz-scattergram URL.

References

  1. Tomasik J, Rahmoune H, Guest PC, Bahn S (2016) Neuroimmune biomarkers in schizophrenia. Schizophr Res 176(1):3–13

    Article  Google Scholar 

  2. Seeman MV (2021) Sex differences in schizophrenia relevant to clinical care. Expert Rev Neurother 21(4):443–453. https://doi.org/10.1080/14737175.2021.1898947

    Article  Google Scholar 

  3. Stilo SA, Murray RM (2019) Non-genetic factors in schizophrenia. Curr Psychiatry Rep 21(10):1–10

    Article  Google Scholar 

  4. Meyer U, Feldon J (2010) Epidemiology-driven neurodevelopmental animal models of schizophrenia. Prog Neurobiol 90(3):285–326

    Article  Google Scholar 

  5. Müller N (2018) Inflammation in schizophrenia: pathogenetic aspects and therapeutic considerations. Schizophr Bull 44(5):973–982

    Article  Google Scholar 

  6. Vickers NJ (2017) Animal communication: when i’m calling you, will you answer too? Curr Biol 27(14):R713–R715

    Article  Google Scholar 

  7. Demir F (2022) Deep autoencoder-based automated brain tumor detection from MRI data. Artificial Intelligence-Based Brain-Computer Interface. Elsevier; p. 317–51

  8. Qureshi SA, Hussain L, Ibrar U, Alabdulkreem E, Nour MK, Alqahtani MS et al (2023) Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans. Sci Rep 13(1):3291

    Article  Google Scholar 

  9. Grover N, Chharia A, Upadhyay R, Longo L (2023) Schizo-Net: a novel Schizophrenia Diagnosis framework using late fusion multimodal deep learning on electroencephalogram-based brain connectivity indices. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol 31. IEEE, pp 464–473. https://doi.org/10.1109/TNSRE.2023.3237375

  10. Göker H (2023) 1D-convolutional neural network approach and feature extraction methods for automatic detection of schizophrenia. Signal Image Video Process 17(5):1–10. https://doi.org/10.1007/s11760-022-02479-7

    Article  Google Scholar 

  11. Khare SK, Bajaj V, Acharya UR (2023) SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals. Physiol Meas 44(3):035005

    Article  Google Scholar 

  12. Hassan F, Hussain SF, Qaisar SM (2023) Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques. Inf Fusion 92:466–478

    Article  Google Scholar 

  13. Thilakvathi B, Devi SS, Bhanu K, Malaippan M (2017) EEG signal complexity analysis for schizophrenia during rest and mental activity. Biomed Res India 28(1):1–9

    Google Scholar 

  14. Bagherzadeh S, Shahabi MS, Shalbaf A (2022) Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 146:105570

    Article  Google Scholar 

  15. Bondugula RK, Sivangi KB, Udgata SK (2022) Identification of schizophrenic individuals using activity records through visualization of recurrent networks. In: Intelligent Systems: Proceedings of ICMIB 2021. Springer Nature, Singapore, pp 653–664. https://doi.org/10.1007/978-981-19-0901-6_57

  16. Khare SK, Bajaj V, Acharya UR (2021) Spwvd-cnn for automated detection of schizophrenia patients using eeg signals. IEEE Trans Instrum Meas 70:1–9

    Article  Google Scholar 

  17. Ilakiyaselvan N, Khan AN, Shahina A (2022) Reconstructed phase space portraits for detecting brain diseases using deep learning. Biomed Signal Process Control 71:103278

    Article  Google Scholar 

  18. Devia C, Mayol-Troncoso R, Parrini J, Orellana G, Ruiz A, Maldonado PE et al (2019) EEG classification during scene free-viewing for schizophrenia detection. IEEE Trans Neural Syst Rehabil Eng 27(6):1193–1199

    Article  Google Scholar 

  19. Singh K, Malhotra J (2022) Prediction of epileptic seizures from spectral features of intracranial EEG recordings using deep learning approach. Multimed Tools Appl 81(20):28875–28898. https://doi.org/10.1007/s11042-022-12611-x

    Article  Google Scholar 

  20. Piryatinska A, Darkhovsky B, Kaplan A (2017) Binary classification of multichannel-EEG records based on the ϵ-complexity of continuous vector functions. Comput Methods Programs Biomed 152:131–139

    Article  Google Scholar 

  21. Ahmedt-Aristizabal D, Fernando T, Denman S, Robinson JE, Sridharan S, Johnston PJ et al (2020) Identification of children at risk of schizophrenia via deep learning and EEG responses. IEEE J Biomed Health Inform 25(1):69–76

    Article  Google Scholar 

  22. Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y (2020) A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans Neural Syst Rehabil Eng 28(11):2390–2400

    Article  Google Scholar 

  23. Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR (2019) Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl Sci 9(14):2870

    Article  Google Scholar 

  24. Aslan Z, Akin M (2022) A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals. Phys Eng Sci Med 45(1):83–96

    Article  Google Scholar 

  25. Bighelli I, Rodolico A, García-Mieres H, Pitschel-Walz G, Hansen W-P, Schneider-Thoma J et al (2021) Psychosocial and psychological interventions for relapse prevention in schizophrenia: a systematic review and network meta-analysis. Lancet Psychiatry 8(11):969–980

    Article  Google Scholar 

  26. Zeng L-L, Wang H, Hu P, Yang B, Pu W, Shen H et al (2018) Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine 30:74–85

    Article  Google Scholar 

  27. Qureshi MNI, Oh J, Lee B (2019) 3D-CNN based discrimination of schizophrenia using resting-state fMRI. Artif Intell Med 98:10–17

    Article  Google Scholar 

  28. Baygin M, Yaman O, Tuncer T, Dogan S, Barua PD, Acharya UR (2021) Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals. Biomed Signal Process Control 70:102936

    Article  Google Scholar 

  29. Aydemir E, Dogan S, Baygin M, Ooi CP, Barua PD, Tuncer T et al (2022) CGP17Pat: automated schizophrenia detection based on a cyclic group of prime order patterns using EEG signals. Healthcare: MDPI 10(4):643. https://doi.org/10.3390/healthcare10040643

  30. North HF, Bruggemann J, Cropley V, Swaminathan V, Sundram S, Lenroot R et al (2021) Increased peripheral inflammation in schizophrenia is associated with worse cognitive performance and related cortical thickness reductions. Eur Arch Psychiatry Clin Neurosci 271(4):595–607

    Article  Google Scholar 

  31. Barua PD, Dogan S, Tuncer T, Baygin M, Acharya UR (2021) Novel automated PD detection system using aspirin pattern with EEG signals. Comput Biol Med 137:104841

    Article  Google Scholar 

  32. Dogan A, Akay M, Barua PD, Baygin M, Dogan S, Tuncer T et al (2021) PrimePatNet87: prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition. Comput Biol Med 138:104867

    Article  Google Scholar 

  33. Kaplan E, Altunisik E, Firat YE, Barua PD, Dogan S, Baygin M et al (2022) Novel nested patch-based feature extraction model for automated Parkinson’s Disease symptom classification using MRI images. Comput Methods Programs Biomed 224:107030

    Article  Google Scholar 

  34. Kaplan E, Ekinci T, Kaplan S, Barua PD, Dogan S, Tuncer T et a. (2022) PFP-LHCINCA: pyramidal fixed-size patch-based feature extraction and chi-square iterative neighborhood component analysis for automated fetal sex classification on ultrasound images. Contrast Media Mol Imaging 2022:6034971. https://doi.org/10.1155/2022/6034971

  35. Edition F (2013) Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc 21(21):591–643

    Google Scholar 

  36. Tuncer T, Dogan S, Özyurt F, Belhaouari SB, Bensmail H (2020) Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice. IEEE Access 8:84532–84540

    Article  Google Scholar 

  37. Taşcı B, Acharya MR, Barua PD, Yildiz AM, Gun MV, Keles T et al (2022) A new lateral geniculate nucleus pattern-based environmental sound classification using a new large sound dataset. Appl Acoust 196:108897

    Article  Google Scholar 

  38. Tasci B, Tasci I (2022) Deep feature extraction based brain image classification model using preprocessed images: PDRNet. Biomed Signal Process Control 78:103948

    Article  Google Scholar 

  39. Zhang M-L, Zhou Z-H (2007) ML-KNN: A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  Google Scholar 

  40. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. Proceedings of the 17th International Conference on Pattern Recognition, 2004 ICPR 2004: IEEE; p. 32–6

  41. Lee J, Xiao L, Schoenholz S, Bahri Y, Novak R, Sohl-Dickstein J et al (2019) Wide neural networks of any depth evolve as linear models under gradient descent. Adv Neur Inform Process Syst 32. https://doi.org/10.1088/1742-5468/abc62b

  42. Li R (2022) An anisotropic wells algorithm based on deep learning MRI of cerebral infarction. In: 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, pp 1014–7

  43. Teoh L, Ihalage AA, Harp S, Al-Khateeb ZF, Michael-Titus AT, Tremoleda JL et al (2022) Deep learning for behaviour classification in a preclinical brain injury model. PLoS one. 17(6):e0268962

    Article  Google Scholar 

  44. Tavakoli N, Karimi Z, AsadiJouzani S, Azizi N, Rezakhani S, Tobeiha A (2022) Machine Learning-Based Brain Diseases Diagnosing in Electroencephalogram Signals, Alzheimer’s, and Parkinson’s. Prognostic Models in Healthcare: AI and Statistical Approaches. Springer; p. 161–91

  45. Yang X, Zhang N, Schrader P (2022) A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. Mach Learn Appl 8:100290

    Google Scholar 

  46. Alves CL, Pineda AM, Roster K, Thielemann C, Rodrigues FA (2022) EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia. J Phys: Complex 3(2):025001

    Google Scholar 

  47. Tuncer SA, Ayyıldız H, Kalaycı M, Tuncer T (2021) Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images. Comput Biol Med 135:104579

    Article  Google Scholar 

  48. Ayyıldız H, Kalaycı M, Tuncer SA, Çınar A, Tuncer T (2022) Automated COVID-19 detection from WBC-DIFF scattergram images with hybrid CNN model using feature selection. Traitement du Signal 39(2):449–458. https://doi.org/10.18280/ts.390206

  49. Bigorra L, Larriba I, Gutiérrez-Gallego R (2020) Abnormal characteristic “round bottom flask” shape volume-based scattergram as a trigger to suspect persistent polyclonal B-cell lymphocytosis. Clin Chim Acta 511:181–188

    Article  Google Scholar 

  50. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 7263–71. https://arxiv.org/abs/1612.08242v1

  51. Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, et al. (2013) Intriguing properties of neural networks. arXiv preprint arXiv:13126199

  52. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  53. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 4510–20

  54. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:160207360

  55. Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 8697–710

  56. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 770–8

  57. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence

  58. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 2818–26

  59. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 1251–8

  60. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 4700–8

  61. Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 6848–56

  62. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556

  63. Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning: PMLR; p. 6105–14

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Acknowledgements

We gratefully acknowledge the Ethics Committee, Firat University data transcription.

Author information

Authors and Affiliations

  1. Vocational School of Technical Sciences, Firat University, Elazig, 23119, Turkey

    Burak Tasci

  2. Department of Psychiatry, Elazig Fethi Sekin City Hospital, Elazig, Turkey

    Gulay Tasci

  3. Department of Biochemistry, Elazig Fethi Sekin City Hospital, Elazig, Turkey

    Hakan Ayyildiz

  4. Biomedical Engineering, Brown University, Providence, RI, USA

    Aditya P. Kamath

  5. School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia

    Prabal Datta Barua

  6. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

    Turker Tuncer & Sengul Dogan

  7. Department of Medicine, Columbia University Irving Medical Center, New York City, NY, USA

    Edward J. Ciaccio

  8. School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia

    Subrata Chakraborty

  9. Center for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Subrata Chakraborty

  10. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

Authors
  1. Burak Tasci
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  2. Gulay Tasci
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  5. Prabal Datta Barua
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  6. Turker Tuncer
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  10. U. Rajendra Acharya
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Contributions

Conceptualization, B.T., G.T., H.A., A.P.K., P.D.B., S.D., T.T., E.J.C., S.C. and U.R.A.; methodology, B.T., G.T., H.A., A.P.K., P.D.B., S.D., T.T., E.J.C., S.C. and U.R.A.; software, S.D. and T.T.; validation, B.T., G.T., H.A., A.P.K., P.D.B., S.D. and T.T.; formal analysis, G.T., B.T., H.A., A.P.K., P.D.B., S.D. and T.T.; investigation, B.T., G.T., H.A., A.P.K., P.D.B., S.D., T.T., E.J.C., S.C. and U.R.A.; resources, B.T., G.T., H.A. and A.P.K.; data curation, B.T., G.T., H.A. and A.P.K.; writing—original draft preparation, B.T., G.T., H.A., A.P.K., P.D.B., S.D., T.T., E.J.C., S.C. and U.R.A.; writing—review and editing, B.T., G.T., H.A., A.P.K., P.D.B., S.D., T.T., E.J.C., S.C. and U.R.A.; visualization, B.T., and G.T.; supervision, U.R.A.; project administration, U.R.A. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Sengul Dogan.

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The study was approved by the local ethical committee, Ethics Committee of Firat University (2022/05–28).

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Informed consent was obtained from all subjects involved in the study.

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Tasci, B., Tasci, G., Ayyildiz, H. et al. Automated schizophrenia detection model using blood sample scattergram images and local binary pattern. Multimed Tools Appl 83, 42735–42763 (2024). https://doi.org/10.1007/s11042-023-16676-0

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