{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T11:01:05Z","timestamp":1785668465176,"version":"3.56.0"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper\u2019s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network\u2019s ability to gather long-distance dependencies for AI, Expectation\u2013Maximization is applied to the cascade network\u2019s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network\u2019s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network\u2019s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019\/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019\/2020 validation sets are 0.897\/0.903, 0.826\/0.828, and 0.781\/0.786, respectively, indicating high performance in BTS.<\/jats:p>","DOI":"10.3389\/fncom.2024.1391025","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T05:11:58Z","timestamp":1712121118000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model"],"prefix":"10.3389","volume":"18","author":[{"given":"Haewon","family":"Byeon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohannad","family":"Al-Kubaisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashit Kumar","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal","family":"Alghayadh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mukesh","family":"Soni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manisha","family":"Bhende","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkata","family":"Chunduri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Suresh Babu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rubal","family":"Jeet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pbiomolbio.2023.07.001","article-title":"A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques","volume":"183","author":"Ahmed","year":"2023","journal-title":"Prog. Biophys. Mol. Biol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1039572","DOI":"10.3389\/fncom.2022.1039572","article-title":"Symmetry as a guiding principle in artificial and brain neural networks","volume":"16","author":"Anselmi","year":"2022","journal-title":"Front. Comput. Neurosci."},{"key":"ref4","first-page":"65","article-title":"The 9th international child neurology congress and the 7th Asian and Oceanian congress of child neurology held in Beijing","volume":"41","author":"Bao","year":"2003","journal-title":"Zhonghua Er Ke Za Zhi"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/B978-012532104-4\/50076-7","article-title":"Thyroid hormones and brain development","volume-title":"Hormones, Brain and Behavior","author":"Bernal","year":"2002"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"238173","DOI":"10.1016\/j.aca.2020.12.048","article-title":"Label-free metabolic clustering through unsupervised pixel classification of multiparametric fluorescent images","volume":"1148","author":"Bianchetti","year":"2021","journal-title":"Anal. Chim. Acta"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"106767","DOI":"10.1016\/j.compeleceng.2020.106767","article-title":"Medical image registration using deep neural networks: a comprehensive review","volume":"87","author":"Boveiri","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"10","DOI":"10.4103\/2153-3539.255259","article-title":"Digital and computational pathology: bring the future into focus","volume":"10","author":"Bui","year":"2019","journal-title":"J. Pathol. Inform."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"126626","DOI":"10.1016\/j.neucom.2023.126626","article-title":"A comprehensive survey on segmentation techniques for retinal vessel segmentation","volume":"556","author":"Cervantes","year":"2023","journal-title":"Neurocomputing"},{"key":"ref10","first-page":"169","article-title":"Chapter six Hox networks and the origins of motor neuron diversity","volume-title":"Current Topics in Developmental Biology","author":"Dasen","year":"2009"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1218895","DOI":"10.3389\/fncom.2023.1218895","article-title":"Machine and deep-learning for computational neuroscience","volume":"17","author":"Dhiman","year":"2023","journal-title":"Front. Comput. Neurosci."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"102527","DOI":"10.1016\/j.bspc.2021.102527","article-title":"LNCDS: a 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation","volume":"67","author":"Dutande","year":"2021","journal-title":"Biomed. Signal Process. Contr."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"447","DOI":"10.3758\/s13415-021-00919-4","article-title":"Meta-control: from psychology to computational neuroscience","volume":"21","author":"Eppinger","year":"2021","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"ref14","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/B978-0-12-816176-0.00021-1","article-title":"Machine learning based imaging biomarkers in large scale population studies: a neuroimaging perspective","volume-title":"Handbook of Medical Image Computing and Computer Assisted Intervention","author":"Erus","year":"2020"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"102313","DOI":"10.1016\/j.compmedimag.2023.102313","article-title":"A review on brain tumor segmentation based on deep learning methods with federated learning techniques","volume":"110","author":"Ahamed","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1080\/21507740.2020.1740352","article-title":"Artificial intelligence in clinical neuroscience: methodological and ethical challenges","volume":"11","author":"Ienca","year":"2020","journal-title":"AJOB Neurosci."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1126\/science.1241812","article-title":"The inhibitory circuit architecture of the lateral hypothalamus orchestrates feeding","volume":"341","author":"Jennings","year":"2013","journal-title":"Science"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1038\/jid.2013.110","article-title":"7th world congress for hair research abstracts","volume":"133","author":"Kondhalkar","year":"2013","journal-title":"J. Invest. Dermatol."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"95938","DOI":"10.1109\/ACCESS.2021.3094132","article-title":"Deep learning and internet of things based lung ailment recognition through coughing spectrograms","volume":"9","author":"Kumar","year":"2021","journal-title":"IEEE Access"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1038\/nature05453","article-title":"Genome-wide atlas of gene expression in the adult mouse brain","volume":"445","author":"Lein","year":"2007","journal-title":"Nature"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"107777","DOI":"10.1016\/j.compbiomed.2023.107777","article-title":"Medical image identification methods: a review","volume":"169","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref22","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/B978-0-323-85240-1.00004-3","article-title":"Neural network for lung cancer diagnosis","volume-title":"Computational Intelligence in Cancer Diagnosis","author":"Liu","year":"2023"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z. Med. Phys."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1016\/j.neures.2006.04.004","article-title":"Neuroscience Society (Neuroscience 2006)","volume":"55","author":"Newsome","year":"2006","journal-title":"Neurosci. Res."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/j.neucom.2022.04.065","article-title":"Medical image segmentation with 3D convolutional neural networks: a survey","volume":"493","author":"Niyas","year":"2022","journal-title":"Neurocomputing"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1243092","DOI":"10.3389\/fncom.2023.1243092","article-title":"Clustering and disease subtyping in neuroscience, towards better methodological adaptations","volume":"17","author":"Poulakis","year":"2021","journal-title":"Front. Comput. Neurosci."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"S139","DOI":"10.1016\/j.euroneuro.2015.09.010","article-title":"Abstracts of the XXIII rd world congress of psychiatric genetics (WCPG): poster abstracts","volume":"27","author":"Salvoro","year":"2017","journal-title":"Eur. Neuropsychopharmacol."},{"key":"ref28","first-page":"283","author":"Seifert","year":"1987"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"127317","DOI":"10.1016\/j.neucom.2024.127317","article-title":"Automated detection and forecasting of covid-19 using deep learning techniques: a review","volume":"577","author":"Shoeibi","year":"2024","journal-title":"Neurocomputing"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"699840","DOI":"10.3389\/fninf.2021.699840","article-title":"Towards bridging the gap between computational intelligence and neuroscience in brain-computer interfaces with a common description of systems and data","volume":"15","author":"Singh","year":"2021","journal-title":"Front. Neuroinform."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"122799","DOI":"10.1016\/j.jns.2023.122799","article-title":"An overview of clinical machine learning applications in neurology","volume":"455","author":"Smith","year":"2023","journal-title":"J. Neurol. Sci."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2022.11.003","article-title":"SYNAPSE: an international roadmap to large brain imaging","volume":"999","author":"Stampfl","year":"2023","journal-title":"Phys. Rep."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"102357","DOI":"10.1016\/j.nicl.2020.102357","article-title":"Comparison and validation of seven white matter hyperintensities segmentation software in elderly patients","volume":"27","author":"Vanderbecq","year":"2020","journal-title":"NeuroImage Clin."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"100188","DOI":"10.1016\/j.biosx.2022.100188","article-title":"Diagnostic and therapeutic approach of artificial intelligence in neuro-oncological diseases","volume":"11","author":"Venkatesan","year":"2022","journal-title":"Biosens Bioelectron X"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"126295","DOI":"10.1016\/j.neucom.2023.126295","article-title":"TISS-net: brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency","volume":"544","author":"Wu","year":"2023","journal-title":"Neurocomputing"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"130161","DOI":"10.1016\/j.fuel.2023.130161","article-title":"Automatic pore structure analysis in organic-rich shale using FIB-SEM and attention U-net","volume":"358","author":"Yasin","year":"2024","journal-title":"Fuel"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2020.11.005","article-title":"Parallel pathway dense neural network with weighted fusion structure for brain tumor segmentation","volume":"425","author":"Ye","year":"2021","journal-title":"Neurocomputing"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.cpet.2021.09.010","article-title":"AI-based detection, classification and prediction\/prognosis in medical imaging: towards radiophenomics","volume":"17","author":"Yousefirizi","year":"2022","journal-title":"PET Clin."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"106496","DOI":"10.1016\/j.compbiomed.2022.106496","article-title":"Multi-task deep learning for medical image computing and analysis: a review","volume":"153","author":"Zhao","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.ymeth.2020.09.007","article-title":"Deep learning of brain magnetic resonance images: a brief review","volume":"192","author":"Zhao","year":"2021","journal-title":"Methods"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2024.1391025\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T05:12:03Z","timestamp":1712121123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2024.1391025\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,3]]},"references-count":40,"alternative-id":["10.3389\/fncom.2024.1391025"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2024.1391025","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,3]]},"article-number":"1391025"}}