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3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes

Yıl 2023, Cilt: 25 Sayı: 73, 1 - 8, 26.01.2023
https://doi.org/10.21205/deufmd.2023257301

Öz

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental health disorders and it is threatening especially to the academic performance of children. Its neurobiological diagnosis is essential for clinicians to treat ADHD patients properly. Along with machine learning algorithms, and neuroimaging technologies, especially functional magnetic resonance imaging is increasingly used as biomarker in attention deficit hyperactivity disorder. Also, machine learning methods have been becoming popular at last times. This study presents an optimized 3-dimensional convolutional neural network to classify functional magnetic resonance imaging volumes into two classes to assist experts in diagnosing ADHD. To demonstrate the importance of extracting 3D relationships of data, the method has been tested on ADHD-200 public datasets and its performance on the hold-out testing datasets has been evaluated. Then the network performance has been compared with several recent ADHD detection convolutional neural networks in the literature. It has been observed that the proposed network has a promising performance.

Kaynakça

  • [1] Polanczyk, G. V.; Willcutt, E. G.; Salum, G. A.; Kieling, C.; Rohde, L. A. ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis, Int. J. Epidemiol., 2014; vol. 43, no. 2, pp. 434_442.
  • [2] Eloyan, A.; Muschelli, J.; Nebel, M. B.; Liu, H.; Han, F.; Zhao, T.; Barber, A.D.; Joel, S.; Pekar, J.J.; Mostofsky, S. H.; Caffo, B. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging, Frontiers Syst. Neurosci., 2012; vol. 6, p. 61.
  • [3] Peng, X.; Lin, P.; Zhang, T.; Wang, J. Extreme learning machine-based classification of ADHD using brain structural MRI data, PLoS ONE, 2013; vol. 8, no. 11, p. e79476.
  • [4] Liu, D.; Yan, C.; Ren, J.; Yao, L.; Kiviniemi, V. J.; Zang, Y. Using coherence to measure regional homogeneity of resting-state FMRI signal, Frontiers in Systems Neuroscience, 2010; 4, Article 24.
  • [5] Yin, W.; Li, L.; Wu, F. X. Deep Learning for Brain Disorder Diagnosis Based on fMRI Images, ScienceDirect, Neurocomputing, 2020; https://doi.org/10.1016/j.neucom.2020.05.113.
  • [6] Krizhevsky, A.; Sutskever, I.; Hinton, G. E. Imagenet classification with deep convolutional neural networks, in Advances in neural information processing systems, 2012; pp. 1097-1105.
  • [7] Korekado, K.; Morie, T.; Nomura, O.; Ando, H.; Nakano, T.; Matsugu, M.; Iwata, A. A convolutional neural network VLSI for image recognition using merged/mixed analog-digital architecture, in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2003; pp. 169-176.
  • [8] Lawrence, S.; Giles, C. L.; Tsoi, A. C.; Back, A. D. Face recognition: A convolutional neural-network approach, IEEE transactions on neural networks, 1997; vol. 8, pp. 98-113.
  • [9] Karpathy, A.; Toderici, G.; Shetty, S.; Leung, T.; Sukthankar, R.; Fei-Fei, L. Large-scale video classification with convolutional neural networks, in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2014; pp. 1725-1732.
  • [10] Simonyan, K.; Zisserman, A. Two-stream convolutional networks for action recognition in videos, in Advances in neural information processing systems, 2014; pp. 568-576.
  • [11] Zhu, B.; Liu, J. Z.; Rosen, B. R.; Rosen, M. S. Image reconstruction by domain transform manifold learning, 2017; arXiv preprint arXiv:1704.08841.
  • [12] Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition, Proceedings of the Ieee, 1998; vol. 86, pp. 2278-2324, Nov.
  • [13] Vu, H.; Kim, H. C.; Lee, J. H. 3D Convolutional Neural Network for Feature Extraction and Classification of fMRI Volumes, IEEE Explore, 2018; 978-1-5386-6859-7.
  • [14] Zou, L.; Zheng, J.; Miao, C.; Mckeown, M. J.; Wang, Z. J. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI, IEEE Access 5, 2017; 23626–23636.
  • [15] Riaz, A.; Asad, M.; Al-Arif, S. M. R.; Alonso, E.; Dima, D.; Corr, P.; Slabaugh, G. Fcnet: A convolutional neural network for calculating functional connectivity from functional mri. In Proceedings of the International Workshop on Connectomics in Neuroimaging, Quebec City, QC, Canada, 2017; pp. 70–78.
  • [16] Riaz, A.; Asad, M.; Al Arif, S. M. R.; Alonso, E.; Dima, D.; Corr, P.; Slabaugh, G. Deep fMRI: An end-to-end deep network for classification of fMRI data, In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018),Washington, DC, USA, 2018; pp. 1419–1422.
  • [17] Zhang, T.; Li, C.; Li, P.; Peng, Y.; Kang, X.; Jiang, C.; Li, F.; Zhu, X.; Yao, D.; Biswal, B.; Xu, P. Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset, Entropy 2020, 22, 893, 2020; doi:10.3390/e22080893.
  • [18] The ADHD-200 Global Competition, 2011; Available online: http://fcon_1000.projects.nitrc.org/indi/adhd200/junk/results.html (accessed on 22 September 2021).
  • [19] Milham, M. P.; Fair, D.; Mennes, M.; Mostofsky, S. H. The ADHD-200 consortium:a model to advance the translational potential of neuroimaging in clinical neuro-science. Front. Syst. Neurosci. 6 (62), 2012; ISSN 1662-5137.http://dx.doi.org/10.3389/fnsys.2012.00062.
  • [20]Centers for Disease Control and Prevention, HIPAA privacy rule and public health. Guidance from CDC and the U.S. Department of Health and Human Services, MMWR, Morbidity Mortality Weekly Rep., 2003; vol. 52.
  • [21] Poldrack, R.; A., Mumford, J. A.; Nichols, T. E. Handbook of Functional MRI Data Analysis, Cambridge University Press, 2011; ISBN 978-0-521-51766-9 (Hardback)
  • [22] Bellec, P.; Chu, C.; Chouinard-Decorte, F.; Benhajali, Y.; Margulies, D. S.; Craddock, R. C. The Neuro Bureau ADHD-200 Preprocessed repistory, Neuroimage, 144, Part B, 2017; pp. 275-286. Doi: 10.1016/j.neuroimage.2016.06.034.
  • [23]Preprocessed Connectome Projects, 2017; Available online: http://preprocessed-connectomes-project.org/adhd200/ (accessed on 22 September 2021).
  • [24]Baratloo, A.; Hosseini, M.; Negida, A.; El Ashal, G. Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity, Emergency(2015); 3(2), 2015: 48-49.

fMRG Hacimlerini Kullanarak DEHB’nin 3B ESA Tabanlı Otomatik Teşhisi

Yıl 2023, Cilt: 25 Sayı: 73, 1 - 8, 26.01.2023
https://doi.org/10.21205/deufmd.2023257301

Öz

Dikkat eksikliği hiperaktivite bozukluğu (DEHB) en sık görülen beyin deformasyonlarından biridir ve özellikle çocukların okul başarılarını olumsuz yönde etkilemektedir. Uzmanların, DEHB hastalarına uygun tedavi verebilmeleri için bu hastalığın nörobiyolojik tanısı önemlidir. Makine öğrenimi algoritmaları ile birlikte, nörogörüntüleme teknolojileri, özellikle fonksiyonel manyetik rezonans görüntüleme, dikkat eksikliği hiperaktivite bozukluğunda biyobelirteç olarak giderek daha fazla kullanılmaktadır. Ayrıca, makine öğrenme yöntemleri son zamanlarda popüler hale gelmektedir. Bu çalışma ile, DEHB tanısında uzmanlara yardımcı olmak amacıyla fonksiyonel manyetik rezonans görüntüleme hacimlerini iki sınıfa ayırmak için optimize edilmiş 3 boyutlu evrişimli bir sinir ağı sunulmaktadır. Verilerin 3 boyutlu ilişkilerinin çıkarılmasının önemini göstermek için yöntem, halka açık ADHD-200 veri setlerinin öğrenme ve test verileri kullanılarak test edilmiş ve sinir ağının performansı değerlendirilmiştir. Daha sonra sinir ağının performansı, literatürdeki birkaç yeni DEHB algılama evrişimli sinir ağı ile karşılaştırılmıştır. Kullanılan sinir ağının umut verici bir performansa sahip olduğu gözlemlenmektedir.

Kaynakça

  • [1] Polanczyk, G. V.; Willcutt, E. G.; Salum, G. A.; Kieling, C.; Rohde, L. A. ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis, Int. J. Epidemiol., 2014; vol. 43, no. 2, pp. 434_442.
  • [2] Eloyan, A.; Muschelli, J.; Nebel, M. B.; Liu, H.; Han, F.; Zhao, T.; Barber, A.D.; Joel, S.; Pekar, J.J.; Mostofsky, S. H.; Caffo, B. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging, Frontiers Syst. Neurosci., 2012; vol. 6, p. 61.
  • [3] Peng, X.; Lin, P.; Zhang, T.; Wang, J. Extreme learning machine-based classification of ADHD using brain structural MRI data, PLoS ONE, 2013; vol. 8, no. 11, p. e79476.
  • [4] Liu, D.; Yan, C.; Ren, J.; Yao, L.; Kiviniemi, V. J.; Zang, Y. Using coherence to measure regional homogeneity of resting-state FMRI signal, Frontiers in Systems Neuroscience, 2010; 4, Article 24.
  • [5] Yin, W.; Li, L.; Wu, F. X. Deep Learning for Brain Disorder Diagnosis Based on fMRI Images, ScienceDirect, Neurocomputing, 2020; https://doi.org/10.1016/j.neucom.2020.05.113.
  • [6] Krizhevsky, A.; Sutskever, I.; Hinton, G. E. Imagenet classification with deep convolutional neural networks, in Advances in neural information processing systems, 2012; pp. 1097-1105.
  • [7] Korekado, K.; Morie, T.; Nomura, O.; Ando, H.; Nakano, T.; Matsugu, M.; Iwata, A. A convolutional neural network VLSI for image recognition using merged/mixed analog-digital architecture, in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2003; pp. 169-176.
  • [8] Lawrence, S.; Giles, C. L.; Tsoi, A. C.; Back, A. D. Face recognition: A convolutional neural-network approach, IEEE transactions on neural networks, 1997; vol. 8, pp. 98-113.
  • [9] Karpathy, A.; Toderici, G.; Shetty, S.; Leung, T.; Sukthankar, R.; Fei-Fei, L. Large-scale video classification with convolutional neural networks, in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2014; pp. 1725-1732.
  • [10] Simonyan, K.; Zisserman, A. Two-stream convolutional networks for action recognition in videos, in Advances in neural information processing systems, 2014; pp. 568-576.
  • [11] Zhu, B.; Liu, J. Z.; Rosen, B. R.; Rosen, M. S. Image reconstruction by domain transform manifold learning, 2017; arXiv preprint arXiv:1704.08841.
  • [12] Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition, Proceedings of the Ieee, 1998; vol. 86, pp. 2278-2324, Nov.
  • [13] Vu, H.; Kim, H. C.; Lee, J. H. 3D Convolutional Neural Network for Feature Extraction and Classification of fMRI Volumes, IEEE Explore, 2018; 978-1-5386-6859-7.
  • [14] Zou, L.; Zheng, J.; Miao, C.; Mckeown, M. J.; Wang, Z. J. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI, IEEE Access 5, 2017; 23626–23636.
  • [15] Riaz, A.; Asad, M.; Al-Arif, S. M. R.; Alonso, E.; Dima, D.; Corr, P.; Slabaugh, G. Fcnet: A convolutional neural network for calculating functional connectivity from functional mri. In Proceedings of the International Workshop on Connectomics in Neuroimaging, Quebec City, QC, Canada, 2017; pp. 70–78.
  • [16] Riaz, A.; Asad, M.; Al Arif, S. M. R.; Alonso, E.; Dima, D.; Corr, P.; Slabaugh, G. Deep fMRI: An end-to-end deep network for classification of fMRI data, In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018),Washington, DC, USA, 2018; pp. 1419–1422.
  • [17] Zhang, T.; Li, C.; Li, P.; Peng, Y.; Kang, X.; Jiang, C.; Li, F.; Zhu, X.; Yao, D.; Biswal, B.; Xu, P. Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset, Entropy 2020, 22, 893, 2020; doi:10.3390/e22080893.
  • [18] The ADHD-200 Global Competition, 2011; Available online: http://fcon_1000.projects.nitrc.org/indi/adhd200/junk/results.html (accessed on 22 September 2021).
  • [19] Milham, M. P.; Fair, D.; Mennes, M.; Mostofsky, S. H. The ADHD-200 consortium:a model to advance the translational potential of neuroimaging in clinical neuro-science. Front. Syst. Neurosci. 6 (62), 2012; ISSN 1662-5137.http://dx.doi.org/10.3389/fnsys.2012.00062.
  • [20]Centers for Disease Control and Prevention, HIPAA privacy rule and public health. Guidance from CDC and the U.S. Department of Health and Human Services, MMWR, Morbidity Mortality Weekly Rep., 2003; vol. 52.
  • [21] Poldrack, R.; A., Mumford, J. A.; Nichols, T. E. Handbook of Functional MRI Data Analysis, Cambridge University Press, 2011; ISBN 978-0-521-51766-9 (Hardback)
  • [22] Bellec, P.; Chu, C.; Chouinard-Decorte, F.; Benhajali, Y.; Margulies, D. S.; Craddock, R. C. The Neuro Bureau ADHD-200 Preprocessed repistory, Neuroimage, 144, Part B, 2017; pp. 275-286. Doi: 10.1016/j.neuroimage.2016.06.034.
  • [23]Preprocessed Connectome Projects, 2017; Available online: http://preprocessed-connectomes-project.org/adhd200/ (accessed on 22 September 2021).
  • [24]Baratloo, A.; Hosseini, M.; Negida, A.; El Ashal, G. Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity, Emergency(2015); 3(2), 2015: 48-49.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Gürcan TAŞPINAR 0000-0003-3932-2683

Nalan ÖZKURT 0000-0002-7970-198X

Yayımlanma Tarihi 26 Ocak 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 25 Sayı: 73

Kaynak Göster

APA TAŞPINAR, G., & ÖZKURT, N. (2023). 3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 25(73), 1-8. https://doi.org/10.21205/deufmd.2023257301
AMA TAŞPINAR G, ÖZKURT N. 3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes. DEUFMD. Ocak 2023;25(73):1-8. doi:10.21205/deufmd.2023257301
Chicago TAŞPINAR, Gürcan, ve Nalan ÖZKURT. “3D CNN Based Automatic Diagnosis of ADHD Using FMRI Volumes”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 25, sy. 73 (Ocak 2023): 1-8. https://doi.org/10.21205/deufmd.2023257301.
EndNote TAŞPINAR G, ÖZKURT N (01 Ocak 2023) 3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 25 73 1–8.
IEEE G. TAŞPINAR ve N. ÖZKURT, “3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes”, DEUFMD, c. 25, sy. 73, ss. 1–8, 2023, doi: 10.21205/deufmd.2023257301.
ISNAD TAŞPINAR, Gürcan - ÖZKURT, Nalan. “3D CNN Based Automatic Diagnosis of ADHD Using FMRI Volumes”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 25/73 (Ocak 2023), 1-8. https://doi.org/10.21205/deufmd.2023257301.
JAMA TAŞPINAR G, ÖZKURT N. 3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes. DEUFMD. 2023;25:1–8.
MLA TAŞPINAR, Gürcan ve Nalan ÖZKURT. “3D CNN Based Automatic Diagnosis of ADHD Using FMRI Volumes”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, c. 25, sy. 73, 2023, ss. 1-8, doi:10.21205/deufmd.2023257301.
Vancouver TAŞPINAR G, ÖZKURT N. 3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes. DEUFMD. 2023;25(73):1-8.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.