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Analysis of the Brainwaves for the Diagnosis of Schizophrenia with Deep Learning Methods

Yıl 2023, Cilt: 5 Sayı: 2, 325 - 337, 27.10.2023

Berkay Serin Sevcan Emek

https://doi.org/10.46387/bjesr.1332678

Öz

Techniques based on the mathematical model of the human brain provide a tracking system for thinking, memory, perception, speech, and other vital activities. This study offers an alternative approach to diagnosing schizophrenia. In this study, data obtained from 14 schizophrenic patients and 14 healthy individuals, using standard 10-20 EEG montages with 19 EEG channels are used. These data are classified in different ways and the findings obtained from the experimental studies are compared in terms of accuracy and time spent. Although the first approach is to conduct the brainwaves with Convolutional Neural Networks (CNN) without processing, it is observed that it does not yield an efficient result, because CNN forgets the wave data associated with each other between each neuron layer. For this reason, Recurrent Neural Network (RNN) is used to maintain the integrity of the data. Throughout the study, brainwaves are classified with suggested deep learning methods and it is tried to reach the most efficient one.

Anahtar Kelimeler

Brainwaves Classification Deep Learning CNN RNN

Kaynakça

  • P. Fusar-Poli, G. Salazar de Pablo, R.P. Rajkumar, A. López-Díaz, S. Malhotra, S. Heckers, S.M. Lawrie, and F. Pillmann “Diagnosis, prognosis, and treatment of brief psychotic episodes: a review and research agenda”, Lancet. Psychiatry, vol. 9, no. 1, pp. 72–83 2022.
  • R.L. Spitzer, and J.L. Fleiss “A re-analysis of the reliability of psychiatric diagnosis”, The British journal of psychiatry : the journal of mental science, vol. 125, no. 0, pp. 341–347, 1974.
  • W. Gaebel, A. Kerst, and J. Stricker “Classification and Diagnosis of Schizophrenia or Other Primary Psychotic Disorders: Changes from ICD-10 to ICD-11 and Implementation in Clinical Practice”, Psychiatria Danubina, vol. 32, no. 3-4, pp. 320–324, 2020.
  • S.B. Guze “Diagnostic and statistical manual of mental disorders: DSM-IV”, American Psychiatric Association, Washinton D.C., 4th Ed., 1994.
  • W. Fenton, L. Mosher, and S. Matthews “Diagnosis of Schizophrenia: A Critical Review of Current Diagnostic Systems Schizophrenia Bulletin”, vol. 7, pp.452–476, 1981.
  • İ.E. Emre, C. Taş, and Ç. Erol, “Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı”, Psikiyatride Güncel Yaklaşımlar-Current Approaches in Psychiatry, vol. 13, no. 2, pp. 332-353, 2021.
  • Y. Bengio, I. Goodfellow, and A. Courville “Deep learning”, MIT press, Cambridge, 2017.
  • G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, and et al. “A survey on deep learning in medical image analysis”, Med Image Anal, vol. 42, pp. 60–88, 2017.
  • Ö. İnik, and E. Ülker “Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri”, Gaziosmanpaşa Bilimsel Araştırma Dergisi (GBAD), vol. 6, no. 3, pp. 85-104, 2017.
  • S. Dong, P. Wang, and K. Abbas “A survey on deep learning and its applications”, Computer Science Review, vol. 40, p. 100379, 2021.
  • L. Deng “A tutorial survey of architectures, algorithms, and applications for deep learning”, APSIPA Transactions on Signal and Information Processing, vol. 3, 2014.
  • M.A. Jatoi, F.A. Dharejo, and S.H. Teevino ”Comparison of machine learning techniques based brain source localization using eeg signals”, Curr Med Imaging, 2020.
  • R. Buettner, D. Beil, S. Scholtz, and A. Djemai “Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings”, In: Proceedings: 53rd Hawaii International Conference on System Sciences, Maui, Hawaii, pp 7–10, 2020.
  • U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, and H. Adeli “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals”, Comput Biol Med, vol. 100, pp. 270–278, 2018.
  • Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T.H. Falk, and J. Faubert “Deep learning-based electroencephalography analysis: a systematic review”, J Neural Eng vol. 16, no. 5, 2019.
  • A. Shalbaf, S. Bagherzadeh, and A. Maghsoudi “Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals”, Physical and Engineering Sciences in Medicine, vol. 43, pp. 1229–1239, 2020.
  • S. Roy, I. Kiral-Kornek, and S. Harrer “ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification”, arXiv:1802.00308, 2018.
  • Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M.S. Lew “Deep learning for visual understanding: a review”, Neurocomputing, vol. 187, pp. 27–48, 2016.
  • G. Litjens, F. Ciompi, J.M. Wolterink, B.D. de Vos, T. Leiner, J. Teuwen, and I. Išgum “State-of-the-art deep learning in cardiovascular image analysis”, JACC Cardiovasc Imaging, vol. 12, no. 8, pp. 1549–1565, 2019.
  • J. Chai, H. Zeng, A. Li, and E.W.T. Ngai “Deep learning in computer vision: A critical review of emerging techniques and application scenarios”, Machine Learning with Applications, vol. 6, 2021.
  • H. Greenspan, B. van Ginneken, and R.M. Summers “Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique”, IEEE Trans Med Imaging, vol. 35, no. 5, pp. 1153–1159, 2016.
  • X. Zhang, L. Yao, X. Wang, J. Monaghan, and D. Mcalpine “A survey on deep learning based brain computer interface: recent advances and new frontiers”, arXiv:1905.04149 2019.
  • K. Simonyan, and A. Zisserman “Very deep convolutional networks for large-scale image recognition”, arXiv preprint arXiv:1409.1556, 2014.
  • C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna “Rethinking the inception architecture for computer vision”, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826, 2016.
  • T.H. McGlashan “Early detection and intervention of schizophrenia: rationale and research”, The British journal of psychiatry. Supplement, vol. 172, no. 33, pp. 3–6, 1998.
  • S. Siuly, Y. Guo, O. F. Alcin, Y. Li, P. Wen, and H. Wang “Exploring deep residual network based features for automatic schizophrenia detection from EEG”, Physical and Engineering Sciences in Medicine, vol. 46, pp. 561–574, 2023.
  • World Health Organization (WHO), “Schizophrenia”, 2022, https://www.who.int/news-room/fact-sheets/detail/schizophrenia. Accessed 6 Sep 2023
  • D-W. Ko, and J-J. Yang “EEG-Based Schizophrenia Diagnosis through Time Series Image Conversion and Deep Learning”, Electronics, vol. 11, no. 14, 2265, 2022,
  • S.K. Khare, V. Bajaj, and U.R. Acharya "SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021.
  • S.L. Oh, J. Vicnesh, E.J. Ciaccio, R. Yuvaraj, and U.R. Acharya “Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals”, Applied Sciences, vol. 9, no. 14, p. 2870, 2019.
  • Z. Aslan, and M. Akin “A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals”, Physical and Engineering Sciences in Medicine, vol. 45, pp. 83–96, 2022.
  • N. Sobahi, B. Ari, H. Cakar, O.F. Alcin, and A. Sengur "A New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordings," in IEEE Sensors Journal, vol. 22, no. 8, pp. 7913-7919, 2022.
  • M. Şeker, and M.S. Özerdem “EEG based Schizophrenia Detection using SPWVD-ViT Model”, European Journal of Technique (EJT), vol. 12, no. 2, pp. 137-144, 2022.
  • M. Sharma, and U.R. Acharya “Automated detection of schizophrenia using optimal wavelet -based l1 norm features extracted from singlechannel EEG,” Cognit. Neurodynamics, pp. 1–4, Dec. 2020.
  • A. Savio, J. Charpentier, M. Termenón, A.K. Shinn, and M. Grana “Neural classifiers for schizophrenia diagnostic support on diffusion imaging data”, Neural Netw World, vol. 20, no. 7, p. 935, 2010.
  • F. Afshani, A. Shalbaf, R. Shalbaf, and J. Sleigh “Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia”, Cognitive Neurodyn, vol. 13, no. 6, pp. 531–540, 2019.
  • A. Saeedi, M. Saeedi, A. Maghsoudi, and A. Shalbaf “Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach”, Cognitive Neurodyn, vol. 15, pp. 239-252, 2021.
  • Z. Dvey-Aharon, N. Fogelson, A. Peled, and N. Intrator “Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach”, PLoS ONE 10:e0123033, 2015.
  • J.W. Kim, Y.S. Lee, D.H. Han, K.J. Min, J. Lee, and K. Lee “Diagnostic utility of quantitative EEG in un-medicated schizophrenia”, Neurosci Lett, vol. 589, pp. 126–131, 2015.
  • E. Olejarczyk, and W. Jernajczyk "EEG in schizophrenia", 2017.
  • V. Jahmunah, S.L. Oh, V. Rajinikanth, E.J. Ciaccio, K. H. Cheong, N. Arunkumar, and U.R. Acharya “Automated detection of schizophrenia using nonlinear signal processing methods”, Artificial Intelligence in Medicine, vol. 100, 2019,
  • C.R. Phang, F. Noman, H. Hussain, C.M. Ting, and H. Ombao "A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 5, pp. 1333-1343, May 2020,
  • Z. Aslan, and M. Akin “Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals”, Traitement du Signal, vol. 37, no. 2, pp. 235-244, 2020.
  • A.N. Chandran, K. Sreekumar, and D.P. Subha “EEG-Based Automated Detection of Schizophrenia Using Long Short-Term Memory (LSTM) Network”, In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore, 2021.
  • T. Çetinkaya Saray, and A. Sertbaş “Derin Öğrenme Algoritmalarının GPU ve CPU Donanım Mimarileri Üzerinde Uygulanması ve Performans Analizi: Deneysel Araştırma”, Avrupa Bilim ve Teknoloji Dergisi, vol. 33, pp. 10-19, 2022.
  • M. Pandey, M. Fernandez, F. Gentile, and et al. “The transformational role of GPU computing and deep learning in drug discovery”, Nat Mach Intell 4, pp. 211–221, 2022.

Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi

Yıl 2023, Cilt: 5 Sayı: 2, 325 - 337, 27.10.2023

Berkay Serin Sevcan Emek

https://doi.org/10.46387/bjesr.1332678

Öz

İnsan beyninin matematiksel modeli üzerine kurulan tekniklerin kullanılması, insan için hayati olan düşünme, hafıza, algılama, konuşma ve diğer yaşam aktivitelerinin sürdürülmesinde bir takip sistemi oluşturmaktadır. Bu çalışmada şizofreni hastalığın teşhis edilmesine yönelik alternatif bir yaklaşım sunulmaktadır. Çalışmada, 14 şizofreni hastası ve 14 sağlıklı bireyden alınmış, 19 EEG kanalıyla standart 10-20 EEG montajı kullanılarak 250 Hz örnekleme frekansı ile elde edilen veriler kullanılmaktadır. Çalışma boyunca, bu veriler farklı şekillerde sınıflandırılmakta ve deneysel çalışmalarla elde edilen bulgular doğruluk ve harcanan süre açısından karşılaştırılmaktadır. İlk yaklaşım olarak, beyin dalgalarının işlenmeden evrişimli sinir ağları (Convolutional Neural Network -CNN) ile yürütmek olsa da CNN her nöron katmanı arasında birbiriyle ilişkili dalga verilerini unuttuğu için verimli bir sonuç vermediği gözlemlenmektedir. Bu nedenle, verilerin bütünlüğünü koruyacak tekrarlayan sinir ağları (Recurrent Neural Network -RNN) kullanılmaktadır. Çalışma genelinde, beyin dalgaları önerilen ve yapılandırılmış derin öğrenme yöntemleri ile sınıflandırılmakta ve en verimli olanına ulaşmaya çalışılmaktadır.

Anahtar Kelimeler

Beyin Dalgası Sınıflama Derin Öğrenme CNN RNN

Kaynakça

  • P. Fusar-Poli, G. Salazar de Pablo, R.P. Rajkumar, A. López-Díaz, S. Malhotra, S. Heckers, S.M. Lawrie, and F. Pillmann “Diagnosis, prognosis, and treatment of brief psychotic episodes: a review and research agenda”, Lancet. Psychiatry, vol. 9, no. 1, pp. 72–83 2022.
  • R.L. Spitzer, and J.L. Fleiss “A re-analysis of the reliability of psychiatric diagnosis”, The British journal of psychiatry : the journal of mental science, vol. 125, no. 0, pp. 341–347, 1974.
  • W. Gaebel, A. Kerst, and J. Stricker “Classification and Diagnosis of Schizophrenia or Other Primary Psychotic Disorders: Changes from ICD-10 to ICD-11 and Implementation in Clinical Practice”, Psychiatria Danubina, vol. 32, no. 3-4, pp. 320–324, 2020.
  • S.B. Guze “Diagnostic and statistical manual of mental disorders: DSM-IV”, American Psychiatric Association, Washinton D.C., 4th Ed., 1994.
  • W. Fenton, L. Mosher, and S. Matthews “Diagnosis of Schizophrenia: A Critical Review of Current Diagnostic Systems Schizophrenia Bulletin”, vol. 7, pp.452–476, 1981.
  • İ.E. Emre, C. Taş, and Ç. Erol, “Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı”, Psikiyatride Güncel Yaklaşımlar-Current Approaches in Psychiatry, vol. 13, no. 2, pp. 332-353, 2021.
  • Y. Bengio, I. Goodfellow, and A. Courville “Deep learning”, MIT press, Cambridge, 2017.
  • G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, and et al. “A survey on deep learning in medical image analysis”, Med Image Anal, vol. 42, pp. 60–88, 2017.
  • Ö. İnik, and E. Ülker “Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri”, Gaziosmanpaşa Bilimsel Araştırma Dergisi (GBAD), vol. 6, no. 3, pp. 85-104, 2017.
  • S. Dong, P. Wang, and K. Abbas “A survey on deep learning and its applications”, Computer Science Review, vol. 40, p. 100379, 2021.
  • L. Deng “A tutorial survey of architectures, algorithms, and applications for deep learning”, APSIPA Transactions on Signal and Information Processing, vol. 3, 2014.
  • M.A. Jatoi, F.A. Dharejo, and S.H. Teevino ”Comparison of machine learning techniques based brain source localization using eeg signals”, Curr Med Imaging, 2020.
  • R. Buettner, D. Beil, S. Scholtz, and A. Djemai “Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings”, In: Proceedings: 53rd Hawaii International Conference on System Sciences, Maui, Hawaii, pp 7–10, 2020.
  • U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, and H. Adeli “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals”, Comput Biol Med, vol. 100, pp. 270–278, 2018.
  • Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T.H. Falk, and J. Faubert “Deep learning-based electroencephalography analysis: a systematic review”, J Neural Eng vol. 16, no. 5, 2019.
  • A. Shalbaf, S. Bagherzadeh, and A. Maghsoudi “Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals”, Physical and Engineering Sciences in Medicine, vol. 43, pp. 1229–1239, 2020.
  • S. Roy, I. Kiral-Kornek, and S. Harrer “ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification”, arXiv:1802.00308, 2018.
  • Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M.S. Lew “Deep learning for visual understanding: a review”, Neurocomputing, vol. 187, pp. 27–48, 2016.
  • G. Litjens, F. Ciompi, J.M. Wolterink, B.D. de Vos, T. Leiner, J. Teuwen, and I. Išgum “State-of-the-art deep learning in cardiovascular image analysis”, JACC Cardiovasc Imaging, vol. 12, no. 8, pp. 1549–1565, 2019.
  • J. Chai, H. Zeng, A. Li, and E.W.T. Ngai “Deep learning in computer vision: A critical review of emerging techniques and application scenarios”, Machine Learning with Applications, vol. 6, 2021.
  • H. Greenspan, B. van Ginneken, and R.M. Summers “Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique”, IEEE Trans Med Imaging, vol. 35, no. 5, pp. 1153–1159, 2016.
  • X. Zhang, L. Yao, X. Wang, J. Monaghan, and D. Mcalpine “A survey on deep learning based brain computer interface: recent advances and new frontiers”, arXiv:1905.04149 2019.
  • K. Simonyan, and A. Zisserman “Very deep convolutional networks for large-scale image recognition”, arXiv preprint arXiv:1409.1556, 2014.
  • C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna “Rethinking the inception architecture for computer vision”, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826, 2016.
  • T.H. McGlashan “Early detection and intervention of schizophrenia: rationale and research”, The British journal of psychiatry. Supplement, vol. 172, no. 33, pp. 3–6, 1998.
  • S. Siuly, Y. Guo, O. F. Alcin, Y. Li, P. Wen, and H. Wang “Exploring deep residual network based features for automatic schizophrenia detection from EEG”, Physical and Engineering Sciences in Medicine, vol. 46, pp. 561–574, 2023.
  • World Health Organization (WHO), “Schizophrenia”, 2022, https://www.who.int/news-room/fact-sheets/detail/schizophrenia. Accessed 6 Sep 2023
  • D-W. Ko, and J-J. Yang “EEG-Based Schizophrenia Diagnosis through Time Series Image Conversion and Deep Learning”, Electronics, vol. 11, no. 14, 2265, 2022,
  • S.K. Khare, V. Bajaj, and U.R. Acharya "SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021.
  • S.L. Oh, J. Vicnesh, E.J. Ciaccio, R. Yuvaraj, and U.R. Acharya “Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals”, Applied Sciences, vol. 9, no. 14, p. 2870, 2019.
  • Z. Aslan, and M. Akin “A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals”, Physical and Engineering Sciences in Medicine, vol. 45, pp. 83–96, 2022.
  • N. Sobahi, B. Ari, H. Cakar, O.F. Alcin, and A. Sengur "A New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordings," in IEEE Sensors Journal, vol. 22, no. 8, pp. 7913-7919, 2022.
  • M. Şeker, and M.S. Özerdem “EEG based Schizophrenia Detection using SPWVD-ViT Model”, European Journal of Technique (EJT), vol. 12, no. 2, pp. 137-144, 2022.
  • M. Sharma, and U.R. Acharya “Automated detection of schizophrenia using optimal wavelet -based l1 norm features extracted from singlechannel EEG,” Cognit. Neurodynamics, pp. 1–4, Dec. 2020.
  • A. Savio, J. Charpentier, M. Termenón, A.K. Shinn, and M. Grana “Neural classifiers for schizophrenia diagnostic support on diffusion imaging data”, Neural Netw World, vol. 20, no. 7, p. 935, 2010.
  • F. Afshani, A. Shalbaf, R. Shalbaf, and J. Sleigh “Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia”, Cognitive Neurodyn, vol. 13, no. 6, pp. 531–540, 2019.
  • A. Saeedi, M. Saeedi, A. Maghsoudi, and A. Shalbaf “Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach”, Cognitive Neurodyn, vol. 15, pp. 239-252, 2021.
  • Z. Dvey-Aharon, N. Fogelson, A. Peled, and N. Intrator “Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach”, PLoS ONE 10:e0123033, 2015.
  • J.W. Kim, Y.S. Lee, D.H. Han, K.J. Min, J. Lee, and K. Lee “Diagnostic utility of quantitative EEG in un-medicated schizophrenia”, Neurosci Lett, vol. 589, pp. 126–131, 2015.
  • E. Olejarczyk, and W. Jernajczyk "EEG in schizophrenia", 2017.
  • V. Jahmunah, S.L. Oh, V. Rajinikanth, E.J. Ciaccio, K. H. Cheong, N. Arunkumar, and U.R. Acharya “Automated detection of schizophrenia using nonlinear signal processing methods”, Artificial Intelligence in Medicine, vol. 100, 2019,
  • C.R. Phang, F. Noman, H. Hussain, C.M. Ting, and H. Ombao "A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 5, pp. 1333-1343, May 2020,
  • Z. Aslan, and M. Akin “Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals”, Traitement du Signal, vol. 37, no. 2, pp. 235-244, 2020.
  • A.N. Chandran, K. Sreekumar, and D.P. Subha “EEG-Based Automated Detection of Schizophrenia Using Long Short-Term Memory (LSTM) Network”, In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore, 2021.
  • T. Çetinkaya Saray, and A. Sertbaş “Derin Öğrenme Algoritmalarının GPU ve CPU Donanım Mimarileri Üzerinde Uygulanması ve Performans Analizi: Deneysel Araştırma”, Avrupa Bilim ve Teknoloji Dergisi, vol. 33, pp. 10-19, 2022.
  • M. Pandey, M. Fernandez, F. Gentile, and et al. “The transformational role of GPU computing and deep learning in drug discovery”, Nat Mach Intell 4, pp. 211–221, 2022.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Derin Öğrenme
Bölüm Araştırma Makaleleri
Yazarlar

Berkay Serin 0009-0001-2834-4417

Sevcan Emek 0000-0003-2207-8418

Erken Görünüm Tarihi 18 Ekim 2023
Yayımlanma Tarihi 27 Ekim 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 5 Sayı: 2

Kaynak Göster

APA Serin, B., & Emek, S. (2023). Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 5(2), 325-337. https://doi.org/10.46387/bjesr.1332678
AMA Serin B, Emek S. Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi. Müh.Bil.ve Araş.Dergisi. Ekim 2023;5(2):325-337. doi:10.46387/bjesr.1332678
Chicago Serin, Berkay, ve Sevcan Emek. “Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri Ile İncelenmesi”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 5, sy. 2 (Ekim 2023): 325-37. https://doi.org/10.46387/bjesr.1332678.
EndNote Serin B, Emek S (01 Ekim 2023) Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi. Mühendislik Bilimleri ve Araştırmaları Dergisi 5 2 325–337.
IEEE B. Serin ve S. Emek, “Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi”, Müh.Bil.ve Araş.Dergisi, c. 5, sy. 2, ss. 325–337, 2023, doi: 10.46387/bjesr.1332678.
ISNAD Serin, Berkay - Emek, Sevcan. “Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri Ile İncelenmesi”. Mühendislik Bilimleri ve Araştırmaları Dergisi 5/2 (Ekim 2023), 325-337. https://doi.org/10.46387/bjesr.1332678.
JAMA Serin B, Emek S. Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi. Müh.Bil.ve Araş.Dergisi. 2023;5:325–337.
MLA Serin, Berkay ve Sevcan Emek. “Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri Ile İncelenmesi”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, c. 5, sy. 2, 2023, ss. 325-37, doi:10.46387/bjesr.1332678.
Vancouver Serin B, Emek S. Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi. Müh.Bil.ve Araş.Dergisi. 2023;5(2):325-37.
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