Research Article
BibTex RIS Cite

YAPISAL BEYİN MR GÖRÜNTÜLERİNDEN EPİLEPSİ HASTALIĞININ TANISINA YÖNELİK YENİ BİR YÖNTEM

Year 2025, Volume: 28 Issue: 4, 2124 - 2138, 03.12.2025

Hilal Arslan , Orhan Bölükbaş , Harun Uğuz

Abstract

Epilepsi, doğru tanı için hassas klinik değerlendirme gerektiren kronik bir nörolojik bozukluktur. Manyetik rezonans görüntüleme (MRG), epilepsi ile ilişkili beyin yapısındaki değişiklikleri belirlemede önemli bir rol oynamaktadır. Bu çalışmada, yapısal beyin MRG verileri kullanılarak epilepsi tanısı için yeni bir yaklaşım sunulmaktadır. İlk olarak, 75 epilepsi hastası ve 121 sağlıklı kontrolden oluşan toplam 196 katılımcıyı içeren özgün bir veri seti oluşturulmuştur. Bu veri seti temel alınarak, beyin bölgelerindeki hacimsel farklılıklar analiz edilerek ayırt edici özellikler belirlenmiştir. Özellik setini iyileştirmek için Mann-Whitney U testi ve Kolmogorov-Smirnov Z testi gibi istatistiksel seçim yöntemleri uygulanmıştır. Bu süreç sonucunda, başlangıçtaki 50 özelliğin 37’si sınıflandırma için en anlamlı olanlar olarak seçilmiştir. Son aşamada, bu özellikler k-en yakın komşu, destek vektör makineleri, çok katmanlı algılayıcı ve rastgele orman gibi çeşitli makine öğrenmesi sınıflandırıcıları ile değerlendirilmiştir. Deneysel sonuçlar, destek vektör makinesinin en iyi performansı elde ettiğini göstermiştir; %95,45 kesinlik, %84 duyarlılık, %89,36 F1-skoru ve %92,34 doğruluk değerleri elde edilmiştir. Mevcut güncel yöntemlerle karşılaştırıldığında, önerilen yaklaşım son derece rekabetçi bir performans sergilemekte ve epilepsi tanısında etkili bir araç olma potansiyelini ortaya koymaktadır.

Keywords

Epilepsi tanısı özellik seçimi segmentasyon makine öğrenmesi.

References

  • Abu Alfeilat, H., Hassanat, A., Lasassmeh, O., Tarawneh, A., Alhasanat, M., Eyal Salman, H., & Prasath, S. (2019). Effects of distance measure choice on k nearest neighbor classifier performance: A review. Big Data, 7, doi:10.1089/big.2018.0175.
  • Adanır, S. S. (2020). Epilepsili Çocuklarda Beyin Hacminin Manyetik Rezonans Görüntüleri Üzerinde Stereolojik Yöntemle Değerlendirilmesi. Journal of the Turkish Epilepsi Society. https://doi.org/10.14744/epilepsi.2020.63644.
  • Alabed, T., & Servi, S. (2025). Multilayer analysis of nicotine-induced gene expression alterations in breast cancer cells using clustering and supervised learning methods. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1558–1573. doi:10.17780/ksujes.1730962
  • Ashburner, J. (2009). Computational anatomy with the SPM software. Magnetic Resonance Imaging, 27(8), 1163–1174. doi:10.1016/j.mri.2009.01.006.
  • Barnes, J., Boyes, R., Lewis, E., Schott, J., Frost, C., Scahill, R., & Fox, N. (2007). Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral. Neurobiology of Aging, 28(11), 1657–1663. doi:10.1016/j.neurobiolaging.2006.07.008.
  • Beheshti, I., Sone, D., Maikusa, N., Kimura, Y., Shigemoto, Y., Sato, N., & Matsuda, H. (2021). Accurate lateralization and classification of MRI negative 18F FDG PET positive temporal lobe epilepsy using double inversion recovery and machine learning. Computers in Biology and Medicine, 137, 104805.
  • Behrens, M. I., Núñez, J. C., Larraín, C., Castillo, R., Cornejo, L., Romero, V., … Stieltjes, B. (2008). P1 317: Diffusion tensor imaging in frontotemporal lobar degeneration. Alzheimer’s & Dementia, 4(4, Suppl.). doi:10.1016/j.jalz.2008.05.908.
  • Bernasconi, N., Duchesne, S., Janke, A., Lerch, J., Collins, D. L., & Bernasconi, A. (2004). Whole brain voxel based statistical analysis of gray matter and white matter in temporal lobe epilepsy. NeuroImage, 23(2), 717–723.
  • Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer.
  • Bonilha, L., Rorden, C., Castellano, G., Cendes, F., & Li, L. M. (2005). Voxel based morphometry of the thalamus in patients with refractory medial temporal lobe epilepsy. NeuroImage, 25(3), 1016–1021.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324.
  • Briellmann, R. S., Jackson, G. D., Kalnins, R., & Berkovic, S. F. (1998). Hemicranial volume deficits in patients with temporal lobe epilepsy with and without hippocampal sclerosis. Epilepsia, 39(11), 1174–1181.
  • Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446–3453.
  • Bulut, M. K., & Diri, B. (2025). Türkçe Sağlık Danışmanlığıda Büyük il Modellerinin Hasta-Doktor İletişiminde Kullanım Potansiyeli. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 802–822. doi:10.17780/ksujes.1613938.
  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. doi:10.1023/A:1009715923555.
  • Cao, B., Tang, Y., Li, J., Zhang, X., Shang, H. F., & Zhou, D. (2013). A meta analysis of voxel based morphometry studies on gray matter volume alteration in juvenile myoclonic epilepsy. Epilepsy Research, 106(3), 370–377.
  • Caplan, R., Levitt, J., Siddarth, P., Wu, K. N., Gurbani, S., Sankar, R., & Shields, W. D. (2009). Frontal and temporal volumes in childhood absence epilepsy. Epilepsia, 50(11), 2466–2472.
  • Caplová, Z., & Svábová, P. (2020). IBM SPSS Statistics. In Statistics and probability in forensic anthropology (pp. 343–352). Elsevier. doi:10.1016/B978 0 12 815764 0.00027 7.
  • Cantor Rivera, D., Khan, A. R., Goubran, M., Mirsattari, S. M., & Peters, T. M. (2015). Detection of temporal lobe epilepsy using support vector machines in multi parametric quantitative MR imaging. Computerized Medical Imaging and Graphics, 41, 14–28.
  • Chen, S., Zhang, J., Ruan, X., Deng, K., Zhang, J., Zou, D., He, X., Li, F., Bin, G., Zeng, H., & Huang, B. (2020). Voxel based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis. Brain Imaging and Behavior, 14(5), 1945–1954.
  • Chang, A. J., Roth, R., Bougioukli, E., Ruber, T., Keller, S. S., Drane, D. L., Gross, R. E., Welsh, J., Abrol, A., Calhoun, V., Karakis, I., Kaestner, E., Weber, B., McDonald, C., Gleichgerrcht, E., & Bonilha, L. (2023). MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer’s disease, and healthy controls. Communications Medicine, 3(1). https://doi.org/10.1038/s43856-023-00262-4
  • Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016). Efficient KNN classification algorithm for big data. Neurocomputing, 195, 143–148. doi:10.1016/j.neucom.2015.08.112.
  • Del Gaizo, J., Mofrad, N., Jensen, J. H., Clark, D., Glenn, R., Helpern, J., & Bonilha, L. (2017). Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain and Behavior, 7(10), e00801.
  • Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. doi:10.1016/j.neuroimage.2012.01.021.
  • Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., … Dale, A. M. (2002). Whole brain segmentation. Neuron, 33(3), 341–355.
  • Goebel, R. (2012). BrainVoyager—past, present, future. NeuroImage, 62(2), 748–756. doi:10.1016/j.neuroimage.2012.01.083.
  • Graham, R., Perriss, R., & Scarsbrook, A. (2005). DICOM demystified: A review of digital file formats and their use in radiological practice. Clinical Radiology, 60(11), 1133–1140. doi:10.1016/j.crad.2005.07.003.
  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. doi:10.1016/0893 6080(89)90020 8.
  • Huang, J., Xu, J., Kang, L., & Zhang, T. (2020). Identifying epilepsy based on deep learning using DKI images. Frontiers in Human Neuroscience, 14, 590815.
  • Hutchinson, E., Pulsipher, D., Dabbs, K., Myers y Gutierrez, A., Sheth, R., Jones, J., … Hermann, B. (2010). Children with new onset epilepsy exhibit diffusion abnormalities in cerebral white matter in the absence of volumetric differences. Epilepsy Research, 88(2–3), 208–214.
  • Kaestner, E., Rao, J., Chang, A. J., Wang, Z. I., Busch, R. M., Keller, S. S., Rüber, T., Drane, D. L., Stoub, T., Gleichgerrcht, E., Bonilha, L., Hasenstab, K., & McDonald, C. (2023). Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy. Neurology, 101(3). https://doi.org/10.1212/wnl.0000000000207411
  • Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(7), 1667–1689. doi:10.1162/089976603321891855.
  • Keihaninejad, S., Heckemann, R. A., Gousias, I. S., Hajnal, J. V., Duncan, J. S., Aljabar, P., … Hammers, A. (2012). Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole brain automatic MRI segmentation. PLoS ONE, 7(4), e33096.
  • Keller, S. S., Gerdes, J. S., Mohammadi, S., Kellinghaus, C., Kugel, H., Deppe, K., … Deppe, M. (2012). Volume estimation of the thalamus using FreeSurfer and stereology: Consistency between methods. Neuroinformatics, 10(4), 341–350.
  • Keller, S. S., Wilke, M., Wieshmann, U. C., Sluming, V. A., & Roberts, N. (2004). Comparison of standard and optimized voxel based morphometry for analysis of brain changes associated with temporal lobe epilepsy. NeuroImage, 23(3), 860–868.
  • Kälviäinen, R., Salmenperä, T., Partanen, K., Vainio, P., Riekkinen, P., Sr., & Pitkänen, A. (1997). MRI volumetry and T2 relaxometry of the amygdala in newly diagnosed and chronic temporal lobe epilepsy. Epilepsy Research, 28(1), 39–50.
  • Khan, H., Alutaibi, A. I., Tejani, G. G., Sharma, S. K., Khan, A. R., Ahmad, F., & Mousavirad, S. J. (2025). MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF). PLOS One, 20(7), e0325126. https://doi.org/10.1371/journal.pone.0325126
  • Kim, J. H., Lee, J. K., Koh, S. B., Lee, S. A., Lee, J. M., Kim, S. I., & Kang, J. K. (2007). Regional grey matter abnormalities in juvenile myoclonic epilepsy: A voxel based morphometry study. NeuroImage, 37(4), 1132–1137.
  • Kinay, D. (2016). Neuroimaging in epilepsy surgery. Journal of the Turkish Epilepsy Society.
  • Klein, A., Mensh, B., Ghosh, S., Tourville, J., & Hirsch, J. (2005). Mindboggle: Automated brain labeling with multiple atlases. BMC Medical Imaging, 5, 7. doi:10.1186/1471 2342 5 7.
  • Kodipaka, S., Vemuri, B. C., Rangarajan, A., Leonard, C. M., Schmallfuss, I., & Eisenschenk, S. (2007). Kernel Fisher discriminant for shape based classification in epilepsy. Medical Image Analysis, 11(1), 79–90.
  • Kemmotsu, N., Girard, H. M., Bernhardt, B. C., Bonilha, L., Lin, J. J., Tecoma, E. S., Iragui, V. J., Hagler, D. J., Jr., Halgren, E., & McDonald, C. R. (2011). MRI analysis in temporal lobe epilepsy: Cortical thinning and white matter disruptions are related to side of seizure onset. Epilepsia, 52(12), 2257–2266.
  • Kos, F. T., Cecen Kaynak, S., Aktürk Esen, S., Arslan, H., & Uncu, D. (2024). Comparison of different machine learning models for predicting long-term overall survival in non-metastatic colorectal cancers. Cureus, 16(12), e75713. doi:10.7759/cureus.75713
  • Kurkcuoglu, A., Zagyapan, R., & Pelin, C. (2010). Stereological evaluation of temporal lobe/telencephalon volume in temporal lobe epilepsy using the Cavalieri principle. Turkish Neurosurgery, 20(3), 358–363.
  • Liu, R. S. N., Lemieux, L., Bell, G. S., Sisodiya, S. M., Bartlett, P. A., Shorvon, S. D., Sander, J. W. A. S., & Duncan, J. S. (2005). Cerebral damage in epilepsy: A population based longitudinal quantitative MRI study. Epilepsia, 46(9), 1482–1494.
  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60. doi:10.1214/aoms/1177730491.
  • Massey, F. J., Jr. (1951). The Kolmogorov–Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78. doi:10.1080/01621459.1951.10500769.
  • Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614.
  • Nguyen, R. D., Kennady, E. H., Smyth, M. D., Zhu, L., Pao, L. P., Swisher, S. K., Rosas, A., Mitra, A., Patel, R. P., Lankford, J., Von Allmen, G., Watkins, M. W., Funke, M. E., & Shah, M. N. (2021). Convolutional neural networks for pediatric refractory epilepsy classification using resting state functional magnetic resonance imaging. World Neurosurgery, 149, e1112–e1122.
  • Norden, A. D., & Blumenfeld, H. (2002). The role of subcortical structures in human epilepsy. Epilepsy & Behavior, 3(3), 219–231.
  • Ochs, A. L., Ross, D. E., Zannoni, M. D., & Abildskov, T. J. (2015). Comparison of automated brain volume measures obtained with NeuroQuant® and FreeSurfer. Journal of Neuroimaging, 25(5), 721–727. doi:10.1111/jon.12229.
  • Osman, R., & Arslan, H. (2025). The application of AI in oncology research in Türkiye: Impact and future directions. Gazi University Journal of Science Part A: Engineering and Innovation, 12(3), 894–917. doi:10.54287/gujsa.1768020
  • O’Dwyer, R., Wehner, T., LaPresto, E., Ping, L., Tkach, J., Noachtar, S., & Diehl, B. (2010). Differences in corpus callosum volume and diffusivity between temporal and frontal lobe epilepsy. Epilepsy & Behavior, 19(3), 376–382.
  • Park, K. M., Han, Y. H., Kim, T. H., Mun, C. W., Shin, K. J., Ha, S. Y., Park, J., Hur, Y. J., Kim, H. Y., Park, S. H., & Kim, S. E. (2015). Cerebellar white matter changes in patients with newly diagnosed partial epilepsy of unknown etiology. Clinical Neurology and Neurosurgery, 138, 25–30.
  • Peng, B., Wu, L., Zhang, L., & Chen, Y. (2014). The relationship between hippocampal volumes and nonverbal memory in patients with medial temporal lobe epilepsy. Epilepsy Research, 108(10), 1839–1844.
  • Peng, S. J., Harnod, T., Tsai, J. Z., Ker, M. D., Chiou, J. C., Chiueh, H., Wu, C. Y., & Hsin, Y. L. (2014). Evaluation of subcortical grey matter abnormalities in patients with MRI negative cortical epilepsy determined through structural and tensor magnetic resonance imaging. BMC Neurology, 14, 104.
  • Princich, J. P., Donnelly Kehoe, P. A., Deleglise, A., Vallejo Azar, M. N., Pascariello, G. O., Seoane, P., Veron Do Santos, J. G., Collavini, S., Nasimbera, A. H., & Kochen, S. (2021). Diagnostic performance of MRI volumetry in epilepsy patients with hippocampal sclerosis supported through a random forest automatic classification algorithm. Frontiers in Neurology, 12, 613967.
  • Pulsipher, D. T., Seidenberg, M., Morton, J. J., Geary, E., Parrish, J., & Hermann, B. (2007). MRI volume loss of subcortical structures in unilateral temporal lobe epilepsy. Epilepsy & Behavior, 11(3), 442–449.
  • Rudie, J. D., Colby, J. B., & Salamon, N. (2015). Machine learning classification of mesial temporal sclerosis in epilepsy patients. Epilepsy Research, 117, 63–69.
  • Salmenperä, T., Kälviäinen, R., Partanen, K., Mervaala, E., & Pitkänen, A. (2000). MRI volumetry of the hippocampus, amygdala, entorhinal cortex, and perirhinal cortex after status epilepticus. Epilepsy Research, 40(2–3), 155–170.
  • Sahebzamani, G., Saffar, M., & Soltanian Zadeh, H. (2019). Machine learning based analysis of structural MRI for epilepsy diagnosis. In 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE.
  • Seven, İ., Çalişkan, C., Köş, F. T., Arslan, H., Aktürk Esen, S., Ceylan, F., & Uncu, D. (2025). Predicting survival outcomes in advanced pancreatic cancer using machine learning methods. Medicine, 104(33), e43904. doi:10.1097/MD.0000000000043904.
  • Sharafi, P., Arslan, H., Ersoy Evans, S., Varan, A., & Ayter, Ş. (2025). A machine learning approach for predicting familial and sporadic disease cases based on clinical symptoms: introduction of a new dataset. Türk Hijiyen ve Tecrübî Biyoloji Dergisi [Turkish Bulletin of Hygiene and Experimental Biology], 82(1), 99–106. doi:10.5505/turkhijyen.2025.06337.
  • Shokouhi, M., Barnes, A., Suckling, J., Moorhead, T. W., Brennan, D., Job, D., … Condon, B. (2011). Assessment of the impact of scanner related factors on brain morphometry analysis with BrainVISA. BMC Medical Imaging, 11, 23. doi:10.1186/1471 2342 11 23.
  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl. 1), S208–S219. doi:10.1016/j.neuroimage.2004.07.051.
  • Taskiran Sag, A., Arslan, H., Yazgi, H., Eroğlu, E., & Ugur, K. S. (2025). Can Vascular Vertigo Be Recognized by Artificial Intelligence Methods? Archives of Neuropsychiatry. https://doi.org/10.29399/npa.29122
  • Theodore, W. H., DeCarli, C., & Gaillard, W. D. (2003). Total cerebral volume is reduced in patients with localization related epilepsy and a history of complex febrile seizures. Archives of Neurology, 60(2), 250–252.
  • Torres Velazquez, M., Hwang, G., Cook, C. J., Hermann, B., Prabhakaran, V., Meyerand, M. E., & McMillan, A. B. (2020). Multi channel deep neural network for temporal lobe epilepsy classification using multimodal MRI data. In 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). IEEE.
  • Üner, Ç., Tekeli, A., & Demir Karacan, C. (2021). Evaluation of the Results of Patients with a First Afebrile Seizure Admitted to a Pediatric Emergency Department. Turkish Journal of Pediatric Emergency and Intensive Care Medicine, 8(3), 146–150. https://doi.org/10.4274/cayd.galenos.2020.71501.
  • van Elst, L. T., Groffmann, M., Ebert, D., & Schulze Bonhage, A. (2009). Amygdala volume loss in patients with dysphoric disorder of epilepsy. Epilepsy & Behavior, 16(1), 105–112.
  • Vapnik, V. N. (1995). The nature of statistical learning theory. New York, NY: Springer. doi:10.1007/978 1 4757 2440 0.
  • Vergun, S., Gaggl, W., Nair, V. A., Suhonen, J. I., Birn, R. M., Ahmed, A. S., Meyerand, M. E., Reuss, J., DeYoe, E. A., & Prabhakaran, V. (2016). Classification and extraction of resting state networks using healthy and epilepsy fMRI data. Frontiers in Neuroscience, 10.
  • Yang, L., Peng, B., Gao, W., A, R., Liu, Y., Liang, J., Zhu, M., Hu, H., Lu, Z., Pang, C., Dai, Y., & Sun, Y. (2024). Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning. Frontiers in Neurology, 15. https://doi.org/10.3389/fneur.2024.1323623.
  • Zelko, F. A., Pardoe, H. R., Blackstone, S. R., Jackson, G. D., & Berg, A. T. (2014). Regional brain volumes and cognition in childhood epilepsy: Does size really matter? Epilepsy Research, 108(4), 692–700.
  • Zhou, B., An, D., Xiao, F., Niu, R., Li, W., Li, W., Tong, X., Kemp, G. J., Zhou, D., Gong, Q., & Lei, D. (2020). Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging. Frontiers in Medicine, 14(5), 630–641.

A NEW METHOD FOR DIAGNOSING EPILEPSY DISEASE FROM STRUCTURAL BRAIN MR IMAGES

Year 2025, Volume: 28 Issue: 4, 2124 - 2138, 03.12.2025

Hilal Arslan , Orhan Bölükbaş , Harun Uğuz

Abstract

Epilepsy is a chronic neurological disorder that requires accurate clinical evaluation for diagnosis. Magnetic resonance imaging (MRI) plays an essential role in identifying structural brain changes associated with epilepsy. In this study, we introduce a new approach for diagnosing epilepsy using structural brain MRI data. First, we constructed a novel dataset that includes 196 participants, comprising 75 patients with epilepsy and 121 healthy controls. Based on this dataset, we developed a method to identify discriminative features by analyzing volumetric variations across brain regions. To refine the feature set, statistical selection methods were applied, specifically the Mann-Whitney U test and the Kolmogorov-Smirnov Z test. Through this process, 37 of the original 50 features were retained as the most relevant for classification. In the final stage, we evaluated these features using several machine learning classifiers, including k-nearest neighbors, support vector machines, multilayer perceptron, and random forest. The experimental results showed that the support vector machine achieved the best performance, with a precision of 95.45%, a recall of 84%, an F1-score of 89.36%, and an accuracy of 92.34%. Compared with existing state-of-the-art methods, the proposed approach demonstrates highly competitive performance, highlighting its potential as an effective diagnostic tool for epilepsy detection.

Keywords

Epilepsy diagnosis feature selection segmentation machine learning.

References

  • Abu Alfeilat, H., Hassanat, A., Lasassmeh, O., Tarawneh, A., Alhasanat, M., Eyal Salman, H., & Prasath, S. (2019). Effects of distance measure choice on k nearest neighbor classifier performance: A review. Big Data, 7, doi:10.1089/big.2018.0175.
  • Adanır, S. S. (2020). Epilepsili Çocuklarda Beyin Hacminin Manyetik Rezonans Görüntüleri Üzerinde Stereolojik Yöntemle Değerlendirilmesi. Journal of the Turkish Epilepsi Society. https://doi.org/10.14744/epilepsi.2020.63644.
  • Alabed, T., & Servi, S. (2025). Multilayer analysis of nicotine-induced gene expression alterations in breast cancer cells using clustering and supervised learning methods. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1558–1573. doi:10.17780/ksujes.1730962
  • Ashburner, J. (2009). Computational anatomy with the SPM software. Magnetic Resonance Imaging, 27(8), 1163–1174. doi:10.1016/j.mri.2009.01.006.
  • Barnes, J., Boyes, R., Lewis, E., Schott, J., Frost, C., Scahill, R., & Fox, N. (2007). Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral. Neurobiology of Aging, 28(11), 1657–1663. doi:10.1016/j.neurobiolaging.2006.07.008.
  • Beheshti, I., Sone, D., Maikusa, N., Kimura, Y., Shigemoto, Y., Sato, N., & Matsuda, H. (2021). Accurate lateralization and classification of MRI negative 18F FDG PET positive temporal lobe epilepsy using double inversion recovery and machine learning. Computers in Biology and Medicine, 137, 104805.
  • Behrens, M. I., Núñez, J. C., Larraín, C., Castillo, R., Cornejo, L., Romero, V., … Stieltjes, B. (2008). P1 317: Diffusion tensor imaging in frontotemporal lobar degeneration. Alzheimer’s & Dementia, 4(4, Suppl.). doi:10.1016/j.jalz.2008.05.908.
  • Bernasconi, N., Duchesne, S., Janke, A., Lerch, J., Collins, D. L., & Bernasconi, A. (2004). Whole brain voxel based statistical analysis of gray matter and white matter in temporal lobe epilepsy. NeuroImage, 23(2), 717–723.
  • Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer.
  • Bonilha, L., Rorden, C., Castellano, G., Cendes, F., & Li, L. M. (2005). Voxel based morphometry of the thalamus in patients with refractory medial temporal lobe epilepsy. NeuroImage, 25(3), 1016–1021.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324.
  • Briellmann, R. S., Jackson, G. D., Kalnins, R., & Berkovic, S. F. (1998). Hemicranial volume deficits in patients with temporal lobe epilepsy with and without hippocampal sclerosis. Epilepsia, 39(11), 1174–1181.
  • Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446–3453.
  • Bulut, M. K., & Diri, B. (2025). Türkçe Sağlık Danışmanlığıda Büyük il Modellerinin Hasta-Doktor İletişiminde Kullanım Potansiyeli. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 802–822. doi:10.17780/ksujes.1613938.
  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. doi:10.1023/A:1009715923555.
  • Cao, B., Tang, Y., Li, J., Zhang, X., Shang, H. F., & Zhou, D. (2013). A meta analysis of voxel based morphometry studies on gray matter volume alteration in juvenile myoclonic epilepsy. Epilepsy Research, 106(3), 370–377.
  • Caplan, R., Levitt, J., Siddarth, P., Wu, K. N., Gurbani, S., Sankar, R., & Shields, W. D. (2009). Frontal and temporal volumes in childhood absence epilepsy. Epilepsia, 50(11), 2466–2472.
  • Caplová, Z., & Svábová, P. (2020). IBM SPSS Statistics. In Statistics and probability in forensic anthropology (pp. 343–352). Elsevier. doi:10.1016/B978 0 12 815764 0.00027 7.
  • Cantor Rivera, D., Khan, A. R., Goubran, M., Mirsattari, S. M., & Peters, T. M. (2015). Detection of temporal lobe epilepsy using support vector machines in multi parametric quantitative MR imaging. Computerized Medical Imaging and Graphics, 41, 14–28.
  • Chen, S., Zhang, J., Ruan, X., Deng, K., Zhang, J., Zou, D., He, X., Li, F., Bin, G., Zeng, H., & Huang, B. (2020). Voxel based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis. Brain Imaging and Behavior, 14(5), 1945–1954.
  • Chang, A. J., Roth, R., Bougioukli, E., Ruber, T., Keller, S. S., Drane, D. L., Gross, R. E., Welsh, J., Abrol, A., Calhoun, V., Karakis, I., Kaestner, E., Weber, B., McDonald, C., Gleichgerrcht, E., & Bonilha, L. (2023). MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer’s disease, and healthy controls. Communications Medicine, 3(1). https://doi.org/10.1038/s43856-023-00262-4
  • Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016). Efficient KNN classification algorithm for big data. Neurocomputing, 195, 143–148. doi:10.1016/j.neucom.2015.08.112.
  • Del Gaizo, J., Mofrad, N., Jensen, J. H., Clark, D., Glenn, R., Helpern, J., & Bonilha, L. (2017). Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain and Behavior, 7(10), e00801.
  • Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. doi:10.1016/j.neuroimage.2012.01.021.
  • Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., … Dale, A. M. (2002). Whole brain segmentation. Neuron, 33(3), 341–355.
  • Goebel, R. (2012). BrainVoyager—past, present, future. NeuroImage, 62(2), 748–756. doi:10.1016/j.neuroimage.2012.01.083.
  • Graham, R., Perriss, R., & Scarsbrook, A. (2005). DICOM demystified: A review of digital file formats and their use in radiological practice. Clinical Radiology, 60(11), 1133–1140. doi:10.1016/j.crad.2005.07.003.
  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. doi:10.1016/0893 6080(89)90020 8.
  • Huang, J., Xu, J., Kang, L., & Zhang, T. (2020). Identifying epilepsy based on deep learning using DKI images. Frontiers in Human Neuroscience, 14, 590815.
  • Hutchinson, E., Pulsipher, D., Dabbs, K., Myers y Gutierrez, A., Sheth, R., Jones, J., … Hermann, B. (2010). Children with new onset epilepsy exhibit diffusion abnormalities in cerebral white matter in the absence of volumetric differences. Epilepsy Research, 88(2–3), 208–214.
  • Kaestner, E., Rao, J., Chang, A. J., Wang, Z. I., Busch, R. M., Keller, S. S., Rüber, T., Drane, D. L., Stoub, T., Gleichgerrcht, E., Bonilha, L., Hasenstab, K., & McDonald, C. (2023). Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy. Neurology, 101(3). https://doi.org/10.1212/wnl.0000000000207411
  • Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(7), 1667–1689. doi:10.1162/089976603321891855.
  • Keihaninejad, S., Heckemann, R. A., Gousias, I. S., Hajnal, J. V., Duncan, J. S., Aljabar, P., … Hammers, A. (2012). Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole brain automatic MRI segmentation. PLoS ONE, 7(4), e33096.
  • Keller, S. S., Gerdes, J. S., Mohammadi, S., Kellinghaus, C., Kugel, H., Deppe, K., … Deppe, M. (2012). Volume estimation of the thalamus using FreeSurfer and stereology: Consistency between methods. Neuroinformatics, 10(4), 341–350.
  • Keller, S. S., Wilke, M., Wieshmann, U. C., Sluming, V. A., & Roberts, N. (2004). Comparison of standard and optimized voxel based morphometry for analysis of brain changes associated with temporal lobe epilepsy. NeuroImage, 23(3), 860–868.
  • Kälviäinen, R., Salmenperä, T., Partanen, K., Vainio, P., Riekkinen, P., Sr., & Pitkänen, A. (1997). MRI volumetry and T2 relaxometry of the amygdala in newly diagnosed and chronic temporal lobe epilepsy. Epilepsy Research, 28(1), 39–50.
  • Khan, H., Alutaibi, A. I., Tejani, G. G., Sharma, S. K., Khan, A. R., Ahmad, F., & Mousavirad, S. J. (2025). MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF). PLOS One, 20(7), e0325126. https://doi.org/10.1371/journal.pone.0325126
  • Kim, J. H., Lee, J. K., Koh, S. B., Lee, S. A., Lee, J. M., Kim, S. I., & Kang, J. K. (2007). Regional grey matter abnormalities in juvenile myoclonic epilepsy: A voxel based morphometry study. NeuroImage, 37(4), 1132–1137.
  • Kinay, D. (2016). Neuroimaging in epilepsy surgery. Journal of the Turkish Epilepsy Society.
  • Klein, A., Mensh, B., Ghosh, S., Tourville, J., & Hirsch, J. (2005). Mindboggle: Automated brain labeling with multiple atlases. BMC Medical Imaging, 5, 7. doi:10.1186/1471 2342 5 7.
  • Kodipaka, S., Vemuri, B. C., Rangarajan, A., Leonard, C. M., Schmallfuss, I., & Eisenschenk, S. (2007). Kernel Fisher discriminant for shape based classification in epilepsy. Medical Image Analysis, 11(1), 79–90.
  • Kemmotsu, N., Girard, H. M., Bernhardt, B. C., Bonilha, L., Lin, J. J., Tecoma, E. S., Iragui, V. J., Hagler, D. J., Jr., Halgren, E., & McDonald, C. R. (2011). MRI analysis in temporal lobe epilepsy: Cortical thinning and white matter disruptions are related to side of seizure onset. Epilepsia, 52(12), 2257–2266.
  • Kos, F. T., Cecen Kaynak, S., Aktürk Esen, S., Arslan, H., & Uncu, D. (2024). Comparison of different machine learning models for predicting long-term overall survival in non-metastatic colorectal cancers. Cureus, 16(12), e75713. doi:10.7759/cureus.75713
  • Kurkcuoglu, A., Zagyapan, R., & Pelin, C. (2010). Stereological evaluation of temporal lobe/telencephalon volume in temporal lobe epilepsy using the Cavalieri principle. Turkish Neurosurgery, 20(3), 358–363.
  • Liu, R. S. N., Lemieux, L., Bell, G. S., Sisodiya, S. M., Bartlett, P. A., Shorvon, S. D., Sander, J. W. A. S., & Duncan, J. S. (2005). Cerebral damage in epilepsy: A population based longitudinal quantitative MRI study. Epilepsia, 46(9), 1482–1494.
  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60. doi:10.1214/aoms/1177730491.
  • Massey, F. J., Jr. (1951). The Kolmogorov–Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78. doi:10.1080/01621459.1951.10500769.
  • Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614.
  • Nguyen, R. D., Kennady, E. H., Smyth, M. D., Zhu, L., Pao, L. P., Swisher, S. K., Rosas, A., Mitra, A., Patel, R. P., Lankford, J., Von Allmen, G., Watkins, M. W., Funke, M. E., & Shah, M. N. (2021). Convolutional neural networks for pediatric refractory epilepsy classification using resting state functional magnetic resonance imaging. World Neurosurgery, 149, e1112–e1122.
  • Norden, A. D., & Blumenfeld, H. (2002). The role of subcortical structures in human epilepsy. Epilepsy & Behavior, 3(3), 219–231.
  • Ochs, A. L., Ross, D. E., Zannoni, M. D., & Abildskov, T. J. (2015). Comparison of automated brain volume measures obtained with NeuroQuant® and FreeSurfer. Journal of Neuroimaging, 25(5), 721–727. doi:10.1111/jon.12229.
  • Osman, R., & Arslan, H. (2025). The application of AI in oncology research in Türkiye: Impact and future directions. Gazi University Journal of Science Part A: Engineering and Innovation, 12(3), 894–917. doi:10.54287/gujsa.1768020
  • O’Dwyer, R., Wehner, T., LaPresto, E., Ping, L., Tkach, J., Noachtar, S., & Diehl, B. (2010). Differences in corpus callosum volume and diffusivity between temporal and frontal lobe epilepsy. Epilepsy & Behavior, 19(3), 376–382.
  • Park, K. M., Han, Y. H., Kim, T. H., Mun, C. W., Shin, K. J., Ha, S. Y., Park, J., Hur, Y. J., Kim, H. Y., Park, S. H., & Kim, S. E. (2015). Cerebellar white matter changes in patients with newly diagnosed partial epilepsy of unknown etiology. Clinical Neurology and Neurosurgery, 138, 25–30.
  • Peng, B., Wu, L., Zhang, L., & Chen, Y. (2014). The relationship between hippocampal volumes and nonverbal memory in patients with medial temporal lobe epilepsy. Epilepsy Research, 108(10), 1839–1844.
  • Peng, S. J., Harnod, T., Tsai, J. Z., Ker, M. D., Chiou, J. C., Chiueh, H., Wu, C. Y., & Hsin, Y. L. (2014). Evaluation of subcortical grey matter abnormalities in patients with MRI negative cortical epilepsy determined through structural and tensor magnetic resonance imaging. BMC Neurology, 14, 104.
  • Princich, J. P., Donnelly Kehoe, P. A., Deleglise, A., Vallejo Azar, M. N., Pascariello, G. O., Seoane, P., Veron Do Santos, J. G., Collavini, S., Nasimbera, A. H., & Kochen, S. (2021). Diagnostic performance of MRI volumetry in epilepsy patients with hippocampal sclerosis supported through a random forest automatic classification algorithm. Frontiers in Neurology, 12, 613967.
  • Pulsipher, D. T., Seidenberg, M., Morton, J. J., Geary, E., Parrish, J., & Hermann, B. (2007). MRI volume loss of subcortical structures in unilateral temporal lobe epilepsy. Epilepsy & Behavior, 11(3), 442–449.
  • Rudie, J. D., Colby, J. B., & Salamon, N. (2015). Machine learning classification of mesial temporal sclerosis in epilepsy patients. Epilepsy Research, 117, 63–69.
  • Salmenperä, T., Kälviäinen, R., Partanen, K., Mervaala, E., & Pitkänen, A. (2000). MRI volumetry of the hippocampus, amygdala, entorhinal cortex, and perirhinal cortex after status epilepticus. Epilepsy Research, 40(2–3), 155–170.
  • Sahebzamani, G., Saffar, M., & Soltanian Zadeh, H. (2019). Machine learning based analysis of structural MRI for epilepsy diagnosis. In 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE.
  • Seven, İ., Çalişkan, C., Köş, F. T., Arslan, H., Aktürk Esen, S., Ceylan, F., & Uncu, D. (2025). Predicting survival outcomes in advanced pancreatic cancer using machine learning methods. Medicine, 104(33), e43904. doi:10.1097/MD.0000000000043904.
  • Sharafi, P., Arslan, H., Ersoy Evans, S., Varan, A., & Ayter, Ş. (2025). A machine learning approach for predicting familial and sporadic disease cases based on clinical symptoms: introduction of a new dataset. Türk Hijiyen ve Tecrübî Biyoloji Dergisi [Turkish Bulletin of Hygiene and Experimental Biology], 82(1), 99–106. doi:10.5505/turkhijyen.2025.06337.
  • Shokouhi, M., Barnes, A., Suckling, J., Moorhead, T. W., Brennan, D., Job, D., … Condon, B. (2011). Assessment of the impact of scanner related factors on brain morphometry analysis with BrainVISA. BMC Medical Imaging, 11, 23. doi:10.1186/1471 2342 11 23.
  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl. 1), S208–S219. doi:10.1016/j.neuroimage.2004.07.051.
  • Taskiran Sag, A., Arslan, H., Yazgi, H., Eroğlu, E., & Ugur, K. S. (2025). Can Vascular Vertigo Be Recognized by Artificial Intelligence Methods? Archives of Neuropsychiatry. https://doi.org/10.29399/npa.29122
  • Theodore, W. H., DeCarli, C., & Gaillard, W. D. (2003). Total cerebral volume is reduced in patients with localization related epilepsy and a history of complex febrile seizures. Archives of Neurology, 60(2), 250–252.
  • Torres Velazquez, M., Hwang, G., Cook, C. J., Hermann, B., Prabhakaran, V., Meyerand, M. E., & McMillan, A. B. (2020). Multi channel deep neural network for temporal lobe epilepsy classification using multimodal MRI data. In 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). IEEE.
  • Üner, Ç., Tekeli, A., & Demir Karacan, C. (2021). Evaluation of the Results of Patients with a First Afebrile Seizure Admitted to a Pediatric Emergency Department. Turkish Journal of Pediatric Emergency and Intensive Care Medicine, 8(3), 146–150. https://doi.org/10.4274/cayd.galenos.2020.71501.
  • van Elst, L. T., Groffmann, M., Ebert, D., & Schulze Bonhage, A. (2009). Amygdala volume loss in patients with dysphoric disorder of epilepsy. Epilepsy & Behavior, 16(1), 105–112.
  • Vapnik, V. N. (1995). The nature of statistical learning theory. New York, NY: Springer. doi:10.1007/978 1 4757 2440 0.
  • Vergun, S., Gaggl, W., Nair, V. A., Suhonen, J. I., Birn, R. M., Ahmed, A. S., Meyerand, M. E., Reuss, J., DeYoe, E. A., & Prabhakaran, V. (2016). Classification and extraction of resting state networks using healthy and epilepsy fMRI data. Frontiers in Neuroscience, 10.
  • Yang, L., Peng, B., Gao, W., A, R., Liu, Y., Liang, J., Zhu, M., Hu, H., Lu, Z., Pang, C., Dai, Y., & Sun, Y. (2024). Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning. Frontiers in Neurology, 15. https://doi.org/10.3389/fneur.2024.1323623.
  • Zelko, F. A., Pardoe, H. R., Blackstone, S. R., Jackson, G. D., & Berg, A. T. (2014). Regional brain volumes and cognition in childhood epilepsy: Does size really matter? Epilepsy Research, 108(4), 692–700.
  • Zhou, B., An, D., Xiao, F., Niu, R., Li, W., Li, W., Tong, X., Kemp, G. J., Zhou, D., Gong, Q., & Lei, D. (2020). Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging. Frontiers in Medicine, 14(5), 630–641.
There are 75 citations in total.

Details

Primary Language English
Subjects Reinforcement Learning
Journal Section Research Article
Authors

Hilal Arslan 0000-0002-6449-6952

Orhan Bölükbaş 0000-0003-2642-3397

Harun Uğuz 0000-0003-4617-202X

Publication Date December 3, 2025
Submission Date September 12, 2025
Acceptance Date October 21, 2025
Published in Issue Year 2025 Volume: 28 Issue: 4

Cite

APA Arslan, H., Bölükbaş, O., & Uğuz, H. (2025). A NEW METHOD FOR DIAGNOSING EPILEPSY DISEASE FROM STRUCTURAL BRAIN MR IMAGES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 2124-2138.
 Download Cover Image