{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T17:02:26Z","timestamp":1775667746645,"version":"3.50.1"},"reference-count":46,"publisher":"American Institute of Mathematical Sciences (AIMS)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MBE"],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p xml:lang=\"fr\">&lt;p&gt;The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span.&lt;\/p&gt;<\/jats:p>","DOI":"10.3934\/mbe.2024344","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T10:16:08Z","timestamp":1733912168000},"page":"7830-7853","source":"Crossref","is-referenced-by-count":4,"title":["A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI"],"prefix":"10.3934","volume":"21","author":[{"given":"M","family":"Nisha","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T","family":"Kannan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Amrita College of Engineering and Technology, Nagercoil, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K","family":"Sivasankari","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2024344-1","doi-asserted-by":"publisher","unstructured":"A. Obenaus, C. J. Yong-Hing, K. A. Tong, G. E. Sarty, A reliable method for measurement and normalization of pediatric hippocampal volumes,  <i>J. Pediatr. Res.<\/i>,  <b>50<\/b> (2001), 124\u2013132. https:\/\/doi.org\/10.1203\/00006450-200107000-00022","DOI":"10.1203\/00006450-200107000-00022"},{"key":"key-10.3934\/mbe.2024344-2","doi-asserted-by":"publisher","unstructured":"D. Shen, S. Moffat, S. M. Resnick, C. Davatzikos, Measuring size and shape of the hippocampus in MR images using a deformable shape model,  <i>Neuroimage<\/i>,  <b>15<\/b> (2002), 422\u2013434. https:\/\/doi.org\/10.1006\/nimg.2001.0987","DOI":"10.1006\/nimg.2001.0987"},{"key":"key-10.3934\/mbe.2024344-3","doi-asserted-by":"publisher","unstructured":"S. Li, F. Shi, F. Pu, X. Li, T. Jiang, S. Xie, Hippocampal shape analysis of Alzheimer disease based on machine learning methods,  <i>J. Neuroradiol.<\/i>,  <b>28<\/b> (2007), 1339\u20131345. https:\/\/doi.org\/10.3174\/ajnr.A0620","DOI":"10.3174\/ajnr.A0620"},{"key":"key-10.3934\/mbe.2024344-4","doi-asserted-by":"publisher","unstructured":"J. H. Morra, Z. Tu, L. G. Apostolova, A. E. Green, A. W. Toga, P. M. Thompson, Comparison of AdaBoost and support vector machines for detecting Alzheimer's disease through automated hippocampal segmentation,  <i>IEEE Trans. Med. Imaging<\/i>,  <b>29<\/b> (2010), 30\u201343. https:\/\/doi.org\/10.1109\/TMI.2009.2021941","DOI":"10.1109\/TMI.2009.2021941"},{"key":"key-10.3934\/mbe.2024344-5","doi-asserted-by":"publisher","unstructured":"H. Wang, J. W. Suh, S. R. Das, J. B. Pluta, C. Craige; P. A. Yushkevich, Multi-atlas segmentation with joint label fusion,  <i>IEEE Trans. Pattern Anal. Mach. Intell.<\/i>,  <b>35<\/b> (2012), 611\u2013623. https:\/\/doi.org\/10.1109\/TPAMI.2012.143","DOI":"10.1109\/TPAMI.2012.143"},{"key":"key-10.3934\/mbe.2024344-6","doi-asserted-by":"publisher","unstructured":"M. Kim, G. Wu, D. Shen, Unsupervised deep learning for hippocampus segmentation in 7.0 Tesla MR images,  <i>Int. Workshop Mach. Learn. Med. Imaging<\/i>,  (2013), 1\u20138. https:\/\/doi.org\/10.1007\/978-3-319-02267-3_1","DOI":"10.1007\/978-3-319-02267-3_1"},{"key":"key-10.3934\/mbe.2024344-7","doi-asserted-by":"publisher","unstructured":"J. Kim, M. C. Valdes-Hernandez, N. A. Royle, J. Park, Hippocampal shape modeling based on a progressive template surface deformation and its verification,  <i>IEEE Trans. Med. Imaging<\/i>,  <b>34<\/b> (2015), 1242\u20131261. https:\/\/doi.org\/10.1109\/TMI.2014.2382581","DOI":"10.1109\/TMI.2014.2382581"},{"key":"key-10.3934\/mbe.2024344-8","doi-asserted-by":"publisher","unstructured":"D. Zarpalas, P. Gkontra, P. Daras, N. Maglaveras, Accurate and fully automatic hippocampus segmentation using subject-specific 3D optimal local maps into a hybrid active contour model,  <i>IEEE J. Transl. Eng. Health Med.<\/i>,  <b>2<\/b> (2016), 1\u201316. https:\/\/doi.org\/10.1109\/JTEHM.2014.2297953","DOI":"10.1109\/JTEHM.2014.2297953"},{"key":"key-10.3934\/mbe.2024344-9","doi-asserted-by":"publisher","unstructured":"S. Sri Devi, A. Mano, R. Asha, MRI brain tumor segmentation and feature extraction using GLCM,  <i>Int. J. Res. Appl. Sci. Eng. Technol.<\/i>,  <b>6<\/b> (2018), 1911\u20131916. https:\/\/doi.org\/10.22214\/ijraset.2018.1297","DOI":"10.22214\/ijraset.2018.1297"},{"key":"key-10.3934\/mbe.2024344-10","doi-asserted-by":"publisher","unstructured":"V. Dill, P. C. Klein, A. R. Franco, M. S. Pinho, Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters,  <i>J. Comput. Biol. Med.<\/i>,  <b>95<\/b> (2018), 90\u201398. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.02.005","DOI":"10.1016\/j.compbiomed.2018.02.005"},{"key":"key-10.3934\/mbe.2024344-11","doi-asserted-by":"publisher","unstructured":"N. Varuna Shree, T. N. R. Kumar, Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network,  <i>Brain Inform.<\/i>,  <b>5<\/b> (2018), 23\u201330. https:\/\/doi.org\/10.1007\/s40708-017-0075-5","DOI":"10.1007\/s40708-017-0075-5"},{"key":"key-10.3934\/mbe.2024344-12","doi-asserted-by":"publisher","unstructured":"E. Gibson, W. Li, C. Sudre, L. Fidon, D. I. Shakir, G. Wang, et al., NiftyNet: a deep-learning platform for medical imaging,  <i>Comput. Methods Programs Biomed.<\/i>,  <b>158<\/b> (2018), 113\u2013122. https:\/\/doi.org\/10.1016\/j.cmpb.2018.01.025","DOI":"10.1016\/j.cmpb.2018.01.025"},{"key":"key-10.3934\/mbe.2024344-13","doi-asserted-by":"publisher","unstructured":"Y. Shao, J. Kim, Y. Gao, Q. Wang, W. Lin, D. Shen, Hippocampal segmentation from longitudinal infant brain MR images via classification-guided boundary regression,  <i>IEEE Access<\/i>,  <b>7<\/b> (2019), 33728\u201333740. https:\/\/doi.org\/10.1109\/ACCESS.2019.2904143","DOI":"10.1109\/ACCESS.2019.2904143"},{"key":"key-10.3934\/mbe.2024344-14","doi-asserted-by":"publisher","unstructured":"A. Basher, K. Y. Choi, J. J. Lee, B. Lee, B. C. Kim, K. H. Lee, et al., Hippocampus localization using a two-stage ensemble Hough convolutional neural network,  <i>IEEE Access<\/i>,  <b>7<\/b> (2019), 73436\u201373447. https:\/\/doi.org\/10.1109\/ACCESS.2019.2920005","DOI":"10.1109\/ACCESS.2019.2920005"},{"key":"key-10.3934\/mbe.2024344-15","doi-asserted-by":"publisher","unstructured":"S. Liu, Y. Wang, X. Yang, B. Lei, L. Liu, S. X. Li, Deep learning in medical ultrasound analysis: a review,  <i>Engineering<\/i>,  <b>5<\/b> (2019), 261\u2013275. https:\/\/doi.org\/10.1016\/j.eng.2018.11.020","DOI":"10.1016\/j.eng.2018.11.020"},{"key":"key-10.3934\/mbe.2024344-16","doi-asserted-by":"publisher","unstructured":"A. Gumaei, M. M. Hassan, M. R. Hassan, A. Alelaiwi, G. Fortino, A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification,  <i>IEEE Access<\/i>,  <b>7<\/b> (2019), 36266\u201336273. https:\/\/doi.org\/10.1109\/ACCESS.2019.2904145","DOI":"10.1109\/ACCESS.2019.2904145"},{"key":"key-10.3934\/mbe.2024344-17","doi-asserted-by":"publisher","unstructured":"Y. Shi, K. Cheng, Z. Liu, Hippocampal subfields segmentation in brain MR images using generative adversarial networks,  <i>Biomed. Eng. Online<\/i>,  <b>18<\/b> (2019), 1\u201312. https:\/\/doi.org\/10.1186\/s12938-019-0623-8","DOI":"10.1186\/s12938-019-0623-8"},{"key":"key-10.3934\/mbe.2024344-18","doi-asserted-by":"publisher","unstructured":"A. S. Lundervold, A. Lundervold, An overview of deep learning in medical imaging focusing on MRI,  <i>J. Med. Phys.<\/i>,  <b>29<\/b> (2019), 102\u2013127. https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002","DOI":"10.1016\/j.zemedi.2018.11.002"},{"key":"key-10.3934\/mbe.2024344-19","unstructured":"S. M. Nisha, A novel computer-aided diagnosis scheme for breast tumor classification,  <i>Int. Res. J. Eng. Technol.<\/i>,  <b>7<\/b> (2020), 718\u2013724."},{"key":"key-10.3934\/mbe.2024344-20","doi-asserted-by":"publisher","unstructured":"N. Safavian, S. A. H. Batouli, M. A. Oghabian, An automatic level set method for hippocampus segmentation in MR images,  <i>Comput. Methods Biomech. Biomed. Eng. Imaging Vis.<\/i>,  <b>8<\/b> (2020), 400\u2013410. https:\/\/doi.org\/10.1080\/21681163.2019.1706054","DOI":"10.1080\/21681163.2019.1706054"},{"key":"key-10.3934\/mbe.2024344-21","doi-asserted-by":"publisher","unstructured":"M. Liu, F. Li, H. Yan, K. Wang, Y. Ma, L. Shen, et al., A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease,  <i>Neuroimage<\/i>,  <b>208<\/b> (2020), 116459. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.116459","DOI":"10.1016\/j.neuroimage.2019.116459"},{"key":"key-10.3934\/mbe.2024344-22","doi-asserted-by":"publisher","unstructured":"M. K. Singh, K. K. Singh, A review of publicly available automatic brain segmentation methodologies, machine learning models, recent advancements, and their comparison,  <i>Ann. Neurosci.<\/i>,  <b>28<\/b> (2021), 82\u201393. https:\/\/doi.org\/10.1177\/0972753121990","DOI":"10.1177\/0972753121990"},{"key":"key-10.3934\/mbe.2024344-23","doi-asserted-by":"publisher","unstructured":"L. Liu, L. Kuang, Y. Ji, Multimodal MRI brain tumor image segmentation using sparse subspace clustering algorithm,  <i>Comput. Math. Methods Med.<\/i>,  (2020), 8620403. https:\/\/doi.org\/10.1155\/2020\/8620403","DOI":"10.1155\/2020\/8620403"},{"key":"key-10.3934\/mbe.2024344-24","doi-asserted-by":"publisher","unstructured":"D. Carmo, B. Silva, C. Yasuda, L. Rittner, R. Lotufo, Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks,  <i>Heliyon<\/i>,  <b>7<\/b> (2021), e06226. https:\/\/doi.org\/10.1016\/j.heliyon.2021.e06226","DOI":"10.1016\/j.heliyon.2021.e06226"},{"key":"key-10.3934\/mbe.2024344-25","doi-asserted-by":"publisher","unstructured":"R. De Feo, E. H\u00e4m\u00e4l\u00e4inen, E. Manninen, R. Immonen, J. M. Valverde, X. E. Ndode-Ekane, et al., Convolutional neural networks enable robust automatic segmentation of the rat hippocampus in mri after traumatic brain injury,  <i>Front. Neurol.<\/i>,  <b>13<\/b> (2022), 820267. https:\/\/doi.org\/10.3389\/fneur.2022.820267","DOI":"10.3389\/fneur.2022.820267"},{"key":"key-10.3934\/mbe.2024344-26","doi-asserted-by":"publisher","unstructured":"M. Nisha, T. Kannan, K. Sivasankari, M. Sabrigiriraj, Automatic hippocampus segmentation model for MRI of human head through semi-supervised generative adversarial networks,  <i>Neuroquantology<\/i>,  <b>20<\/b> (2022), 5222\u20135232. https:\/\/doi.org\/10.14704\/nq.2022.20.6.NQ22528","DOI":"10.14704\/nq.2022.20.6.NQ22528"},{"key":"key-10.3934\/mbe.2024344-27","doi-asserted-by":"publisher","unstructured":"K. S. Chuang, H. L. Tzeng, S. Chen, J. Wu, T. J. Chen, Fuzzy c-means clustering with spatial information for image segmentation,  <i>Comput. Med. Imaging Graph.<\/i>,  <b>30<\/b> (2006), 9\u201315. https:\/\/doi.org\/10.1016\/j.compmedimag.2005.10.001","DOI":"10.1016\/j.compmedimag.2005.10.001"},{"key":"key-10.3934\/mbe.2024344-28","doi-asserted-by":"publisher","unstructured":"B. N. Li, C. K. Chui, S. Chang, S. H. Ong, Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation,  <i>Comput. Biol. Med.<\/i>,  <b>41<\/b> (2011), 1\u201310. https:\/\/doi.org\/10.1016\/j.compbiomed.2010.10.007","DOI":"10.1016\/j.compbiomed.2010.10.007"},{"key":"key-10.3934\/mbe.2024344-29","doi-asserted-by":"publisher","unstructured":"C. Militello, L. Rundo, M. Dimarco, A. Orlando, V. Conti, R. Woitek, et al., Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering,  <i>Biomed. Signal Process. Control<\/i>,  <b>71<\/b> (2022), 103113. https:\/\/doi.org\/10.1016\/j.bspc.2021.103113","DOI":"10.1016\/j.bspc.2021.103113"},{"key":"key-10.3934\/mbe.2024344-30","unstructured":"Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, J. Liang, U-Net++: A nested U-Net architecture for medical image segmentation, in <i>Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support<\/i>, Springer, (2018), 3\u201311. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1\">https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1<\/ext-link>"},{"key":"key-10.3934\/mbe.2024344-31","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in <i>Proc. IEEE Conf. Comput. Vis. Pattern Recognit.,<\/i>   (2016), 770\u2013778. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1109\/CVPR.2016.90\">https:\/\/doi.org\/10.1109\/CVPR.2016.90<\/ext-link>","DOI":"10.1109\/CVPR.2016.90"},{"key":"key-10.3934\/mbe.2024344-32","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten., K. Q. Weinberger, Densely connected convolutional networks, in <i>Proc. IEEE Conf. Comput. Vis. Pattern Recognit.,<\/i>   (2017), 4700\u20134708. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1109\/CVPR.2017.243\">https:\/\/doi.org\/10.1109\/CVPR.2017.243<\/ext-link>","DOI":"10.1109\/CVPR.2017.243"},{"key":"key-10.3934\/mbe.2024344-33","unstructured":"Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, et al., Swin transformer: Hierarchical vision transformer using shifted windows, in <i>Proc. IEEE\/CVF Int. Conf. Comput. Vis.<\/i>, (2021), 10012\u201310022. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986\">https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986<\/ext-link>"},{"key":"key-10.3934\/mbe.2024344-34","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-Excitation networks, in <i>Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.<\/i>, (2018), 7132\u20137141. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.48550\/arXiv.1709.01507\">https:\/\/doi.org\/10.48550\/arXiv.1709.01507<\/ext-link>"},{"key":"key-10.3934\/mbe.2024344-35","doi-asserted-by":"publisher","unstructured":"J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, et al., Deep high-resolution representation learning for visual recognition,  <i>IEEE Trans. Pattern Anal. Mach. Intell.<\/i>,  <b>43<\/b> (2020), 3349\u20133364. https:\/\/doi.org\/10.1109\/TPAMI.2020.2983686","DOI":"10.1109\/TPAMI.2020.2983686"},{"key":"key-10.3934\/mbe.2024344-36","doi-asserted-by":"publisher","unstructured":"Z. Szentimrey, A. Al\u2010Hayali, S. de Ribaupierre, A. Fenster, E. Ukwatta, Semi\u2010supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound,  <i>Med. Phys.<\/i>, 2024. https:\/\/doi.org\/10.1002\/mp.17242","DOI":"10.1002\/mp.17242"},{"key":"key-10.3934\/mbe.2024344-37","unstructured":"Z. Wang, C. Ma, Dual-contrastive dual-consistency dual-transformer: A semi-supervised approach to medical image segmentation, in <i>Proc. 2023 IEEE\/CVF Int. Conf. Comput. Vis. Workshops<\/i>, (2023), 870\u2013879. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1109\/ICCVW60793.2023.00094\">https:\/\/doi.org\/10.1109\/ICCVW60793.2023.00094<\/ext-link>"},{"key":"key-10.3934\/mbe.2024344-38","doi-asserted-by":"publisher","unstructured":"L. Huang, S. Ruan, T. Den\u0153ux, Semi-supervised multiple evidence fusion for brain tumor segmentation,  <i>Neurocomputing<\/i>,  <b>535<\/b> (2023), 40\u201352. https:\/\/doi.org\/10.1016\/j.neucom.2023.02.047","DOI":"10.1016\/j.neucom.2023.02.047"},{"key":"key-10.3934\/mbe.2024344-39","unstructured":"Z. Wang, I. Voiculescu, Exigent examiner and mean teacher: An advanced 3d cnn-based semi-supervised brain tumor segmentation framework, in <i>Med. Image Learn. Limited Noisy Data: 2nd Int. Workshop MILLanD 2023,<\/i>   (2023), 181\u2013190. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1007\/978-3-031-44917-8_17\">https:\/\/doi.org\/10.1007\/978-3-031-44917-8_17<\/ext-link>"},{"key":"key-10.3934\/mbe.2024344-40","doi-asserted-by":"publisher","unstructured":"G. Qu, B. Lu, J. Shi, Z. Wang, Y. Yuan, Y. Xia, et al., Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation,  <i>Phys. Med. Biol.<\/i>,  <b>69<\/b> (2024), 5. https:\/\/doi.org\/10.1088\/1361-6560\/ad2634","DOI":"10.1088\/1361-6560\/ad2634"},{"key":"key-10.3934\/mbe.2024344-41","doi-asserted-by":"publisher","unstructured":"R. A. Hazarika, A. K. Maji, R. Syiem, S. N. Sur, D. Kandar, Hippocampus segmentation using U-net convolutional network from brain magnetic resonance imaging (MRI),  <i>J. Digit. Imaging,<\/i>   <b>35<\/b> (2022), 893\u2013909. https:\/\/doi.org\/10.1007\/s10278-022-00613-y","DOI":"10.1007\/s10278-022-00613-y"},{"key":"key-10.3934\/mbe.2024344-42","doi-asserted-by":"publisher","unstructured":"D. Ataloglou, A. Dimou, D. Zarpalas, P. Daras, Fast and precise hippocampus segmentation through deep convolutional neural network ensembles and transfer learning,  <i>Neuroinformatics<\/i>,  <b>17<\/b> (2019), 563\u2013582. https:\/\/doi.org\/10.1007\/s12021-019-09417-y","DOI":"10.1007\/s12021-019-09417-y"},{"key":"key-10.3934\/mbe.2024344-43","doi-asserted-by":"publisher","unstructured":"M. Nisha, T. Kannan, K. Sivasankari, Deep integration model: A robust autonomous segmentation technique for hippocampus in MRI images of human head,  <i>Int. J. Health Sci.<\/i>,  <b>6<\/b> (2022), 13745\u201313758. https:\/\/doi.org\/10.53730\/ijhs.v6nS2.8756","DOI":"10.53730\/ijhs.v6nS2.8756"},{"key":"key-10.3934\/mbe.2024344-44","unstructured":"N. Allinson, H. Yin, L. Allinson, J. Slack, <i>Advances in Self-Organising Maps,<\/i>  Springer, 2001. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1007\/978-1-4471-0715-6\">https:\/\/doi.org\/10.1007\/978-1-4471-0715-6<\/ext-link>."},{"key":"key-10.3934\/mbe.2024344-45","unstructured":"S. N. Sivanandam, S. Sumathi, S. N. Deepa, <i>Applications of Fuzzy Logic: Introduction to Fuzzy Logic Using MATLAB,<\/i> Springer, 2007. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1007\/978-3-540-35781-0_8\">https:\/\/doi.org\/10.1007\/978-3-540-35781-0_8<\/ext-link>"},{"key":"key-10.3934\/mbe.2024344-46","doi-asserted-by":"publisher","unstructured":"V. Conti, C. Militello, L. Rundo, S. Vitabile, A novel bio-inspired approach for high-performance management in service-oriented networks,  <i>IEEE Trans. Emerg. Top. Comput.<\/i>,  <b>9<\/b> (2021), 1709\u20131722. https:\/\/doi.org\/10.1109\/TETC.2020.3018312","DOI":"10.1109\/TETC.2020.3018312"}],"container-title":["Mathematical Biosciences and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2024344?viewType=html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T03:26:16Z","timestamp":1734578776000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2024344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":46,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.3934\/mbe.2024344","relation":{},"ISSN":["1551-0018"],"issn-type":[{"value":"1551-0018","type":"print"}],"subject":[],"published":{"date-parts":[[2024]]}}}