{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:17:46Z","timestamp":1787851066455,"version":"build-2784847793"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.neucom.2026.133886","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:14:50Z","timestamp":1778339690000},"page":"133886","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MTA-HXCN: Modified Triplet Attention Fused Hybrid Learning Enabled Explainable Convolutional Neural Network for brain tumor detection"],"prefix":"10.1016","volume":"694","author":[{"given":"Devanshu","family":"Dube","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashok","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133886_bib1","article-title":"MRI brain tumor detection using deep learning and machine learning approaches","volume":"31","author":"Anantharajan","year":"2024","journal-title":"Meas. Sens."},{"issue":"9","key":"10.1016\/j.neucom.2026.133886_bib2","doi-asserted-by":"crossref","first-page":"3579","DOI":"10.1007\/s13042-024-02110-w","article-title":"A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images","volume":"15","author":"Pacal","year":"2024","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.neucom.2026.133886_bib3","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/RBME.2022.3185292","article-title":"Image segmentation for MR brain tumor detection using machine learning: a review","volume":"16","author":"Soomro","year":"2022","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10.1016\/j.neucom.2026.133886_bib4","doi-asserted-by":"crossref","first-page":"42868","DOI":"10.1109\/ACCESS.2024.3379136","article-title":"Optimized brain tumor detection: a dual-module approach for mri image enhancement and tumor classification","volume":"12","author":"Asiri","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133886_bib5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.cmpb.2018.09.006","article-title":"Fusion based glioma brain tumor detection and segmentation using ANFIS classification","volume":"166","author":"Selvapandian","year":"2018","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"02","key":"10.1016\/j.neucom.2026.133886_bib6","first-page":"58","article-title":"Segmentation using AI for identifying tumors in brain MRI","volume":"5","author":"Prasad","year":"2023","journal-title":"Am. J. Multi-Discip. Res. Dev."},{"key":"10.1016\/j.neucom.2026.133886_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106405","article-title":"Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools","volume":"152","author":"Ranjbarzadeh","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2026.133886_bib8","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.cmpb.2019.05.015","article-title":"Brain tumor detection using statistical and machine learning method","volume":"177","author":"Amin","year":"2019","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.neucom.2026.133886_bib9","series-title":"2019 3rd Int. Conf. Trends. Electron. Inf.","first-page":"1289","article-title":"Design and implementing brain tumor detection using machine learning approach","author":"Hemanth","year":"2019"},{"issue":"12","key":"10.1016\/j.neucom.2026.133886_bib10","first-page":"187","article-title":"Brain tumor detection using KNN","volume":"10","author":"Aiwale","year":"2019","journal-title":"Int. J. Sci. Eng. Res."},{"key":"10.1016\/j.neucom.2026.133886_bib11","series-title":"2016 2nd Int. Conf. Adv. Technol. signal. Image. Proc.","first-page":"297","article-title":"Deep random forest-based learning transfer to SVM for brain tumor segmentation","author":"Amiri","year":"2016"},{"key":"10.1016\/j.neucom.2026.133886_bib12","series-title":"2017 Third Int. Conf. Sens. Signal. Proc. Secur.","first-page":"318","article-title":"Brain tumor detection using SVM classifier","author":"Kumar","year":"2017"},{"issue":"1","key":"10.1016\/j.neucom.2026.133886_bib13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s12559-024-10387-w","article-title":"A novel interpretable graph convolutional neural network for multimodal brain tumor segmentation","volume":"17","author":"Arshad Choudhry","year":"2025","journal-title":"Cogn. Comput."},{"key":"10.1016\/j.neucom.2026.133886_bib14","series-title":"2025 Int. Conf. Intell. Syst. Comput. Networks","first-page":"1","article-title":"Adaptive kernel optimization for probabilistic learning: integrating support vector machines with Gaussian process frameworks","author":"Kanth","year":"2025"},{"key":"10.1016\/j.neucom.2026.133886_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2024.102994","article-title":"Enhanced TumorNet: leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans","volume":"24","author":"Zafar","year":"2024","journal-title":"Results Eng."},{"key":"10.1016\/j.neucom.2026.133886_bib16","doi-asserted-by":"crossref","first-page":"2968","DOI":"10.1016\/j.procs.2025.04.556","article-title":"Hybrid deep transfer learning for enhanced brain tumor detection through the integration of MobileNetV2 and InceptionV3","volume":"258","author":"Ogundokun","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.neucom.2026.133886_bib17","first-page":"30","article-title":"Deep CNN based brain tumor detection in intelligent systems","volume":"5","author":"Gupta","year":"2024","journal-title":"Int. J. Intell. Netw."},{"key":"10.1016\/j.neucom.2026.133886_bib18","series-title":"2019 1st Int. conf. Adv. Sci. Eng. Rob. Technol.","first-page":"1","article-title":"Brain tumor detection using convolutional neural network","author":"Hossain","year":"2019"},{"issue":"1","key":"10.1016\/j.neucom.2026.133886_bib19","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-50505-6","article-title":"Detection and classification of brain tumor using hybrid deep learning models","volume":"13","author":"Babu Vimala","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2026.133886_bib20","doi-asserted-by":"crossref","first-page":"42868","DOI":"10.1109\/ACCESS.2024.3379136","article-title":"Optimized brain tumor detection: a dual-module approach for mri image enhancement and tumor classification","volume":"12","author":"Asiri","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133886_bib21","doi-asserted-by":"crossref","first-page":"65426","DOI":"10.1109\/ACCESS.2022.3184113","article-title":"A robust approach for brain tumor detection in magnetic resonance images using finetuned efficientnet","volume":"10","author":"Shah","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133886_bib22","article-title":"MRI brain tumor detection and classification using parallel deep convolutional neural networks","volume":"26","author":"Rahman","year":"2023","journal-title":"Meas. Sens."},{"key":"10.1016\/j.neucom.2026.133886_bib23","doi-asserted-by":"crossref","first-page":"116942","DOI":"10.1109\/ACCESS.2021.3105874","article-title":"Vgg-scnet: a vgg net-based deep learning framework for brain tumor detection on mri images","volume":"9","author":"Majib","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133886_bib24","article-title":"SAlexNet: superimposed AlexNet using residual attention mechanism for accurate and efficient automatic primary brain tumor detection and classification","volume":"25","author":"Qureshi","year":"2025","journal-title":"Results Eng."},{"issue":"1","key":"10.1016\/j.neucom.2026.133886_bib25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s12880-024-01195-7","article-title":"A hybrid deep CNN model for brain tumor image multi-classification","volume":"24","author":"Srinivasan","year":"2024","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133886_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2025.100413","article-title":"MobDenseNet: a hybrid deep learning model for brain tumor classification using MRI","author":"Afroj","year":"2025","journal-title":"Array"},{"key":"10.1016\/j.neucom.2026.133886_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109893","article-title":"A lightweight attention-driven YOLOv5m model for improved brain tumor detection","volume":"188","author":"Muksimova","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2026.133886_bib28","article-title":"Enhancing brain tumor segmentation in MRI images: a hybrid approach using UNet, attention mechanisms, and transformers","volume":"27","author":"Nguyen-Tat","year":"2024","journal-title":"Egypt. Inf. J."},{"key":"10.1016\/j.neucom.2026.133886_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.aej.2024.11.063","article-title":"FL-SiCNN: an improved brain tumor diagnosis using siamese convolutional neural network in a peer-to-peer federated learning approach","volume":"114","author":"Onaizah","year":"2025","journal-title":"Alex. Eng. J."},{"key":"10.1016\/j.neucom.2026.133886_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107838","article-title":"Brain tumor detection with bi-directional cascade Gaussian kernel feature-generative adversarial networks","volume":"107","author":"Anjana","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"10.1016\/j.neucom.2026.133886_bib31","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/a18020089","article-title":"GATransformer: a graph attention network-based transformer model to generate explainable attentions for brain tumor detection","volume":"18","author":"Tehsin","year":"2025","journal-title":"Algorithms"},{"issue":"1","key":"10.1016\/j.neucom.2026.133886_bib32","doi-asserted-by":"crossref","first-page":"28669","DOI":"10.1038\/s41598-025-11574-x","article-title":"Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches","volume":"15","author":"Banerjee","year":"2025","journal-title":"Sci. Rep."},{"issue":"pt. A","key":"10.1016\/j.neucom.2026.133886_bib33","article-title":"Towards accurate and interpretable brain tumor diagnosis: T-FSPANNet with tri-attribute and pyramidal attention-based feature fusion","volume":"113","author":"Pacal","year":"2026","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.neucom.2026.133886_bib34","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3555638","article-title":"Transforming brain tumor detection empowering multi-class classification with vision transformers and efficientnetv2","author":"Tariq","year":"2025","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.neucom.2026.133886_bib35","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.1093\/comjnl\/bxad104","article-title":"Leveraging meta-learning to improve unsupervised domain adaptation","volume":"67","author":"Farhadi","year":"2024","journal-title":"Comput. J."},{"key":"10.1016\/j.neucom.2026.133886_bib36","doi-asserted-by":"crossref","DOI":"10.1109\/TFUZZ.2025.3555281","article-title":"Active domain adaptation based on probabilistic fuzzy C-means clustering for pancreatic tumor segmentation","author":"Qin","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst. b"},{"key":"10.1016\/j.neucom.2026.133886_bib37","unstructured":"BraTS2020 dataset: \u3008https:\/\/www.med.upenn.edu\/cbica\/brats2020\/data.html\u3009."},{"key":"10.1016\/j.neucom.2026.133886_bib38","unstructured":"BraTS2024 dataset: \u3008https:\/\/www.synapse.org\/Synapse:syn53708249\/wiki\/627759\u3009."},{"key":"10.1016\/j.neucom.2026.133886_bib39","doi-asserted-by":"crossref","first-page":"183525","DOI":"10.1109\/ACCESS.2024.3450593","article-title":"Brain tumor segmentation using generative adversarial networks","volume":"12","author":"Ali","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.neucom.2026.133886_bib40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/medicina59010119","article-title":"A novel generative adversarial network-based approach for automated brain tumour segmentation","volume":"59","author":"Sille","year":"2023","journal-title":"Medicina"},{"key":"10.1016\/j.neucom.2026.133886_bib41","series-title":"2018 Int. Conf. Dev. Appl. Syst.","first-page":"138","article-title":"Detection the mid-sagittal plane in brain slice MR images by using local ternary pattern","author":"Baji","year":"2018"},{"issue":"5","key":"10.1016\/j.neucom.2026.133886_bib42","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1109\/LSP.2018.2817176","article-title":"LOOP descriptor: local optimal-oriented pattern","volume":"25","author":"Chakraborti","year":"2018","journal-title":"IEEE Signal Process Lett."},{"issue":"1","key":"10.1016\/j.neucom.2026.133886_bib43","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40708-025-00257-y","article-title":"Explainable CNN for brain tumor detection and classification through XAI based key features identification","volume":"12","author":"Iftikhar","year":"2025","journal-title":"Brain Inf."},{"issue":"8","key":"10.1016\/j.neucom.2026.133886_bib44","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.3390\/diagnostics12081850","article-title":"Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): review, challenges, and future perspectives","volume":"12","author":"Xie","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.neucom.2026.133886_bib45","series-title":"2020 Int. joint Conf. Neural Networks","first-page":"1","article-title":"Eigen-cam: class activation map using principal components","author":"Muhammad","year":"2020"},{"key":"10.1016\/j.neucom.2026.133886_bib46","series-title":"2025 3rd International Conference on Device Intelligence, Comput. Commun. Technol.","first-page":"39","article-title":"Exploring explainable AI in brain tumor detection: a hybrid approach using EfficientNet and CBAM","author":"Mir","year":"2025"},{"key":"10.1016\/j.neucom.2026.133886_bib47","series-title":"Data-Driven Clin. Decis. Making Using Deep Learn. Imaging","first-page":"25","article-title":"Advancing brain tumour detection: transfer learning-based approach fused with squeeze-and-excitation (SE) attention mechanism in computer vision","author":"Shovon","year":"2024"},{"issue":"1","key":"10.1016\/j.neucom.2026.133886_bib48","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1186\/s12880-024-01323-3","article-title":"Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach","volume":"24","author":"AG","year":"2024","journal-title":"BMC Med. Imaging"},{"issue":"12","key":"10.1016\/j.neucom.2026.133886_bib49","doi-asserted-by":"crossref","first-page":"3884","DOI":"10.1109\/TMI.2022.3199032","article-title":"Multimodal triplet attention network for brain disease diagnosis","volume":"41","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133886_bib50","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JRFID.2023.3349193","article-title":"Brain tumor MRI segmentation method based on improved Res-UNet","volume":"8","author":"Li","year":"2024","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"10.1016\/j.neucom.2026.133886_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107047","article-title":"A novel SLCA-UNet architecture for automatic MRI brain tumor segmentation","volume":"100","author":"Tejashwini","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"issue":"19","key":"10.1016\/j.neucom.2026.133886_bib52","doi-asserted-by":"crossref","first-page":"5993","DOI":"10.3390\/s25195993","article-title":"FALS-YOLO: an efficient and lightweight method for automatic brain tumor detection and segmentation","volume":"25","author":"Sun","year":"2025","journal-title":"Sensors"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122601283X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122601283X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:22:28Z","timestamp":1787847748000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092523122601283X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":52,"alternative-id":["S092523122601283X"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133886","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MTA-HXCN: Modified Triplet Attention Fused Hybrid Learning Enabled Explainable Convolutional Neural Network for brain tumor detection","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133886","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133886"}}