{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:18:31Z","timestamp":1778692711977,"version":"3.51.4"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100008675","name":"Zayed University","doi-asserted-by":"crossref","award":["23010"],"award-info":[{"award-number":["23010"]}],"id":[{"id":"10.13039\/501100008675","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08535-0","type":"journal-article","created":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T04:27:21Z","timestamp":1778387241000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A CNN-based method with capuchin search algorithm-based weighted constrained optimization for brain tumor classification"],"prefix":"10.1007","volume":"82","author":[{"given":"Dina","family":"Tbaishat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Tubishat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malik","family":"Braik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Azmi","family":"Al-Betar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"issue":"8","key":"8535_CR1","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s11227-025-07509-y","volume":"81","author":"M Al-Shalabi","year":"2025","unstructured":"Al-Shalabi M, Mahdi MA, Braik M, Al-Betar MA, Ahamad S, Saad SA (2025) Feasibility analysis and opposition white shark optimizer for optimizing modified efficientnetv2 model for road crack classification: M. al-shalabi et al. J Supercomput 81(8):1015","journal-title":"J Supercomput"},{"key":"8535_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2025.3569208","author":"A-S Mohammed","year":"2025","unstructured":"Mohammed A-S, Mahdi Mohammed A, Malik B, Azmi A-BM, Shahanawaj A, Saad Sawsan A (2025) Opposition-based white shark optimizer for optimizing modified efficientnetv2 in road crack classification. IEEE Open J Comput Soc. https:\/\/doi.org\/10.1109\/OJCS.2025.3569208","journal-title":"IEEE Open J Comput Soc"},{"issue":"6","key":"8535_CR3","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s10586-025-05220-4","volume":"28","author":"O Drogham","year":"2025","unstructured":"Drogham O, Ryalat MH, Al-Najdawi N, Alkhawaldeh RS, AlShaqsi J, Al-Betar MA (2025) Dynamic colormap visualization integrated with Harris Hawks optimization for enhanced lung CT segmentation and diagnostic precision. Clust Comput 28(6):377","journal-title":"Clust Comput"},{"key":"8535_CR4","doi-asserted-by":"publisher","first-page":"102423","DOI":"10.1016\/j.artmed.2022.102423","volume":"133","author":"C Combi","year":"2022","unstructured":"Combi C, Amico B, Bellazzi R, Holzinger A, Moore JH, Zitnik M, Holmes JH (2022) A manifesto on explainability for artificial intelligence in medicine. Artif Intell Med 133:102423","journal-title":"Artif Intell Med"},{"key":"8535_CR5","doi-asserted-by":"crossref","unstructured":"Ahanger AN, Nisa BU, Ahanger AB, Aalam SW, Bashir S, Macha MA, Bhat MR (2025) Artificial intelligence and the use of imaging modalities in biomedical sciences. In: Artificial Intelligence in Human Health and Diseases, pp. 145\u2013169. Springer","DOI":"10.1007\/978-981-96-8176-1_9"},{"key":"8535_CR6","doi-asserted-by":"publisher","first-page":"116255","DOI":"10.1016\/j.psychres.2024.116255","volume":"342","author":"C Gauld","year":"2024","unstructured":"Gauld C, Martin Vincent P, Bottemanne H, Fourneret P, Jean-Arthur M-F, Guillaume D (2024) Exploring the interplay of clinical reasoning and artificial intelligence in psychiatry: current insights and future directions. Psychiatry Res 342:116255","journal-title":"Psychiatry Res"},{"key":"8535_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3449690","author":"C Liu","year":"2024","unstructured":"Liu C, Cheng S, Shi M, Shah A, Bai W, Arcucci R (2024) Imitate: clinical prior guided hierarchical vision-language pre-training. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1109\/TMI.2024.3449690","journal-title":"IEEE Trans Med Imaging"},{"key":"8535_CR8","doi-asserted-by":"publisher","first-page":"108917","DOI":"10.1016\/j.compbiomed.2024.108917","volume":"179","author":"N Kalra","year":"2024","unstructured":"Kalra N, Verma P, Verma S (2024) Advancements in AI based healthcare techniques with focus on diagnostic techniques. Comput Biol Med 179:108917","journal-title":"Comput Biol Med"},{"key":"8535_CR9","doi-asserted-by":"crossref","unstructured":"Qin J, Liu C, Cheng S, Guo Y, Arcucci R (2024) Freeze the backbones: a parameter-efficient contrastive approach to robust medical vision-language pre-training. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1686\u20131690. IEEE","DOI":"10.1109\/ICASSP48485.2024.10447326"},{"key":"8535_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3332217","author":"L Che","year":"2023","unstructured":"Che L, Sibo C, Weiping D, Rossella A (2023) Spectral cross-domain neural network with soft-adaptive threshold spectral enhancement. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3332217","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8535_CR11","doi-asserted-by":"publisher","first-page":"107051","DOI":"10.1016\/j.compbiomed.2023.107051","volume":"162","author":"NS Gupta","year":"2023","unstructured":"Gupta NS, Kumar P (2023) Perspective of artificial intelligence in healthcare data management: a journey towards precision medicine. Comput Biol Med 162:107051","journal-title":"Comput Biol Med"},{"key":"8535_CR12","doi-asserted-by":"crossref","unstructured":"Braik M, Al-Hiary H, Al-Betar MA(2023) Brain tumor segmentation of MRI images based on k-means and white shark optimizer. In: 2023 24th International Arab Conference on Information Technology (ACIT), pp. 1\u20138. IEEE","DOI":"10.1109\/ACIT58888.2023.10453855"},{"issue":"3","key":"8535_CR13","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s40998-021-00426-9","volume":"45","author":"E Irmak","year":"2021","unstructured":"Irmak E (2021) Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran J Sci Technol Trans Electr Eng 45(3):1015\u20131036","journal-title":"Iran J Sci Technol Trans Electr Eng"},{"key":"8535_CR14","doi-asserted-by":"publisher","first-page":"101576","DOI":"10.1016\/j.imu.2024.101576","volume":"50","author":"CM Umarani","year":"2024","unstructured":"Umarani CM, Gollagi SG, Allagi S, Sambrekar K, Ankali Sanjay B (2024) Advancements in deep learning techniques for brain tumor segmentation: a survey. Inf Med Unlocked 50:101576","journal-title":"Inf Med Unlocked"},{"key":"8535_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-025-10416-3","author":"M Saeed","year":"2025","unstructured":"Saeed M, Sarah O, Abdel-Aziz M (2025) Deep learning and machine learning for brain tumor detection: a review, challenges, and future directions. Arch Comput Methods Eng. https:\/\/doi.org\/10.1007\/s11831-025-10416-3","journal-title":"Arch Comput Methods Eng"},{"key":"8535_CR16","doi-asserted-by":"publisher","first-page":"101393","DOI":"10.1016\/j.imu.2023.101393","volume":"43","author":"JA Shaqsi","year":"2023","unstructured":"Shaqsi JA, Drogham O, Aburass S (2023) Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset. Inform Med Unlocked 43:101393","journal-title":"Inform Med Unlocked"},{"issue":"5","key":"8535_CR17","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1007\/s12652-021-03544-8","volume":"13","author":"H Habib","year":"2022","unstructured":"Habib H, Amin R, Ahmed B, Hannan A (2022) Hybrid algorithms for brain tumor segmentation, classification and feature extraction. J Ambient Intell Humaniz Comput 13(5):2763\u20132784","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"8535_CR18","doi-asserted-by":"publisher","first-page":"103863","DOI":"10.1016\/j.bspc.2022.103863","volume":"78","author":"GS Sunsuhi","year":"2022","unstructured":"Sunsuhi GS, Albin Jose S (2022) An adaptive eroded deep convolutional neural network for brain image segmentation and classification using inception resnetv2. Biomed Signal Process Control 78:103863","journal-title":"Biomed Signal Process Control"},{"key":"8535_CR19","unstructured":"Cheng J (2017) Brain magnetic resonance imaging tumor dataset. Figshare MRI dataset version, 5"},{"key":"8535_CR20","unstructured":"Bhuvaji S, Kadam A, Bhumkar P, Dedge S, Kanchan S (2020) Brain tumor classification (mri). Kaggle 10 https:\/\/www.kaggle.com\/datasets\/sartajbhuvaji\/brain-tumor-classification-mri. [Online; accessed 19 June 2024]"},{"key":"8535_CR21","unstructured":"Pradeep (2021) Brain MRI. https:\/\/www.kaggle.com\/datasets\/pradeep2665\/brain-mri\/. [Online; accessed 19 July 2024]"},{"key":"8535_CR22","unstructured":"Sherif M (2020) Brain tumor dataset. https:\/\/www.kaggle.com\/datasets\/mohamedmetwalysherif\/braintumordataset\/ [Online; accessed 19 June 2024]"},{"issue":"3","key":"8535_CR23","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1007\/s11831-024-10188-2","volume":"32","author":"N Rasool","year":"2025","unstructured":"Rasool N, Bhat JI (2025) A critical review on segmentation of glioma brain tumor and prediction of overall survival. Arch Comput Methods Eng 32(3):1525\u20131569","journal-title":"Arch Comput Methods Eng"},{"key":"8535_CR24","doi-asserted-by":"publisher","first-page":"105539","DOI":"10.1016\/j.compbiomed.2022.105539","volume":"146","author":"NF Aurna","year":"2022","unstructured":"Aurna NF, Yousuf MA, Taher KA, Azad AKM, Moni MA (2022) A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models. Comput Biol Med 146:105539","journal-title":"Comput Biol Med"},{"key":"8535_CR25","doi-asserted-by":"publisher","first-page":"103631","DOI":"10.1016\/j.bspc.2022.103631","volume":"76","author":"KS Ananda Kumar","year":"2022","unstructured":"Ananda Kumar KS, Prasad AY, Metan J (2022) A hybrid deep cnn-cov-19-res-net transfer learning architype for an enhanced brain tumor detection and classification scheme in medical image processing. Biomed Signal Process Control 76:103631","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"8535_CR26","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.18280\/ts.380428","volume":"38","author":"S Kuraparthi","year":"2021","unstructured":"Kuraparthi S, Reddy MK, Sujatha CN, Valiveti H, Duggineni C, Kollati M, Kora P et al (2021) Brain tumor classification of MRI images using deep convolutional neural network. Traitement du Signal 38(4):1171\u20131179","journal-title":"Traitement du Signal"},{"issue":"10","key":"8535_CR27","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"key":"8535_CR28","unstructured":"fernando2rad. Brain tumor MRI images 44 classes. https:\/\/www.kaggle.com\/datasets\/fernando2rad\/brain-tumor-mri-images-44c, (2023). Kaggle dataset, Accessed 09 Jan 2026"},{"key":"8535_CR29","doi-asserted-by":"publisher","first-page":"101641","DOI":"10.1016\/j.bspc.2019.101641","volume":"55","author":"S Nema","year":"2020","unstructured":"Nema S, Dudhane A, Murala S, Naidu S (2020) Rescuenet: an unpaired Gan for brain tumor segmentation. Biomed Signal Process Control 55:101641","journal-title":"Biomed Signal Process Control"},{"issue":"6","key":"8535_CR30","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1002\/jemt.23688","volume":"84","author":"T Sadad","year":"2021","unstructured":"Sadad T, Rehman A, Munir A, Saba T, Tariq U, Ayesha N, Abbasi R (2021) Brain tumor detection and multi-classification using advanced deep learning techniques. Microsc Res Tech 84(6):1296\u20131308","journal-title":"Microsc Res Tech"},{"issue":"10","key":"8535_CR31","doi-asserted-by":"publisher","first-page":"7498","DOI":"10.3390\/curroncol29100590","volume":"29","author":"S Tummala","year":"2022","unstructured":"Tummala S, Kadry S, Bukhari SAC, Rauf HT (2022) Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling. Curr Oncol 29(10):7498\u20137511","journal-title":"Curr Oncol"},{"key":"8535_CR32","doi-asserted-by":"publisher","first-page":"120534","DOI":"10.1016\/j.eswa.2023.120534","volume":"230","author":"MA Talukder","year":"2023","unstructured":"Talukder MA, Islam MM, Uddin MA, Arnisha Akhter Md, Pramanik AJ, Aryal S, Almoyad MAA, Hasan KF, Moni MA (2023) An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert Syst Appl 230:120534","journal-title":"Expert Syst Appl"},{"key":"8535_CR33","doi-asserted-by":"publisher","first-page":"122347","DOI":"10.1016\/j.eswa.2023.122347","volume":"238","author":"A Akter","year":"2024","unstructured":"Akter A, Nosheen N, Ahmed S, Hossain M, Yousuf MA, Almoyad MAA, Hasan KF, Moni MA (2024) Robust clinical applicable CNN and u-net based algorithm for MRI classification and segmentation for brain tumor. Expert Syst Appl 238:122347","journal-title":"Expert Syst Appl"},{"key":"8535_CR34","doi-asserted-by":"publisher","first-page":"102117","DOI":"10.1016\/j.rineng.2024.102117","volume":"22","author":"M Agarwal","year":"2024","unstructured":"Agarwal M, Rani G, Kumar A, Kumar P, Manikandan R, Gandomi AH (2024) Deep learning for enhanced brain tumor detection and classification. Results Eng 22:102117","journal-title":"Results Eng"},{"key":"8535_CR35","doi-asserted-by":"publisher","first-page":"822666","DOI":"10.3389\/fgene.2022.822666","volume":"13","author":"L Gaur","year":"2022","unstructured":"Gaur L, Bhandari M, Razdan T, Mallik S, Zhao Z (2022) Explanation-driven deep learning model for prediction of brain tumour status using MRI image data. Front Genet 13:822666","journal-title":"Front Genet"},{"key":"8535_CR36","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger Kilian\u00a0Q (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"3","key":"8535_CR37","doi-asserted-by":"publisher","first-page":"300","DOI":"10.4103\/jnsbm.JNSBM_12_3_5","volume":"12","author":"P Rath","year":"2021","unstructured":"Rath P, Mallick PK, Siddavatam R, Chae GS (2021) An empirical development of hyper-tuned CNN using spotted hyena optimizer for bio-medical image classification. J Nat Sci Biol Med 12(3):300\u2013316","journal-title":"J Nat Sci Biol Med"},{"issue":"1","key":"8535_CR38","doi-asserted-by":"publisher","first-page":"372","DOI":"10.3390\/s22010372","volume":"22","author":"MF Alanazi","year":"2022","unstructured":"Alanazi MF, Ali MU, Hussain SJ, Zafar A, Mohatram M, Irfan M, AlRuwaili R, Alruwaili M, Ali NH, Albarrak AM (2022) Brain tumor\/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model. Sensors 22(1):372","journal-title":"Sensors"},{"issue":"1","key":"8535_CR39","doi-asserted-by":"publisher","first-page":"14876","DOI":"10.1038\/s41598-025-95803-3","volume":"15","author":"QUA Ishfaq","year":"2025","unstructured":"Ishfaq QUA, Bibi R, Ali A, Jamil F, Saeed Y, Alnashwan RO, Chelloug SA, Muthanna MSA (2025) Automatic smart brain tumor classification and prediction system using deep learning. Sci Rep 15(1):14876","journal-title":"Sci Rep"},{"key":"8535_CR40","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11063-020-10398-2","volume":"53","author":"W Ayadi","year":"2021","unstructured":"Ayadi W, Elhamzi W, Charfi I, Atri M (2021) Deep CNN for brain tumor classification. Neural Process Lett 53:671\u2013700","journal-title":"Neural Process Lett"},{"key":"8535_CR41","unstructured":"The cancer imaging archive (TCIA) public access. https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/ (2020). Accessed 09 Jan 2026"},{"key":"8535_CR42","unstructured":"SartajBhuvaji (2024) Brain tumor classification (MRI). https:\/\/www.kaggle.com\/datasets\/sartajbhuvaji\/brain-tumor-classification-mri, Kaggle dataset. Accessed 09 Jan 2026"},{"issue":"7","key":"8535_CR43","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1007\/s00521-020-05145-6","volume":"33","author":"M Braik","year":"2021","unstructured":"Braik M, Sheta A, Al-Hiary H (2021) A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm. Neural Comput Appl 33(7):2515\u20132547","journal-title":"Neural Comput Appl"},{"key":"8535_CR44","doi-asserted-by":"crossref","unstructured":"Chelghoum R, Ikhlef A, Hameurlaine A, Jacquir S (2020) Transfer learning using convolutional neural network architectures for brain tumor classification from MRI images. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 189\u2013200. Springer","DOI":"10.1007\/978-3-030-49161-1_17"},{"key":"8535_CR45","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.cogsys.2019.09.007","volume":"59","author":"T Saba","year":"2020","unstructured":"Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 59:221\u2013230","journal-title":"Cogn Syst Res"},{"issue":"22","key":"8535_CR46","doi-asserted-by":"publisher","first-page":"11514","DOI":"10.3390\/app122211514","volume":"12","author":"J Zheng","year":"2022","unstructured":"Zheng J, Gao Y, Zhang H, Lei Y, Zhang J (2022) Otsu multi-threshold image segmentation based on improved particle swarm algorithm. Appl Sci 12(22):11514","journal-title":"Appl Sci"},{"issue":"4","key":"8535_CR47","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1002\/ima.22433","volume":"30","author":"T Kalaiselvi","year":"2020","unstructured":"Kalaiselvi T, Padmapriya T, Sriramakrishnan P, Priyadharshini V (2020) Development of automatic glioma brain tumor detection system using deep convolutional neural networks. Int J Imaging Syst Technol 30(4):926\u2013938","journal-title":"Int J Imaging Syst Technol"},{"key":"8535_CR48","doi-asserted-by":"publisher","first-page":"100929","DOI":"10.1016\/j.rineng.2023.100929","volume":"17","author":"S Arvind","year":"2023","unstructured":"Arvind S, Tembhurne JV, Diwan T, Sahare P (2023) Improvised light weight deep CNN based u-net for the semantic segmentation of lungs from chest x-rays. Results Eng 17:100929","journal-title":"Results Eng"},{"issue":"3","key":"8535_CR49","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1002\/ima.22408","volume":"30","author":"M Agarwal","year":"2020","unstructured":"Agarwal M, Rani G, Dhaka VS (2020) Optimized contrast enhancement for tumor detection. Int J Imaging Syst Technol 30(3):687\u2013703","journal-title":"Int J Imaging Syst Technol"},{"issue":"3","key":"8535_CR50","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.compeleceng.2013.06.013","volume":"40","author":"P Shanmugavadivu","year":"2014","unstructured":"Shanmugavadivu P, Balasubramanian K (2014) Thresholded and optimized histogram equalization for contrast enhancement of images. Comput Electr Eng 40(3):757\u2013768","journal-title":"Comput Electr Eng"},{"issue":"24","key":"8535_CR51","doi-asserted-by":"publisher","first-page":"4868","DOI":"10.1016\/j.ijleo.2015.09.161","volume":"126","author":"MF Khan","year":"2015","unstructured":"Khan MF, Khan E, Abbasi ZA (2015) Image contrast enhancement using normalized histogram equalization. Optik 126(24):4868\u20134875","journal-title":"Optik"},{"issue":"4","key":"8535_CR52","doi-asserted-by":"publisher","first-page":"11017","DOI":"10.1007\/s11042-023-16016-2","volume":"83","author":"S Chakraverti","year":"2024","unstructured":"Chakraverti S, Agarwal P, Pattanayak HS, Chauhan SPS, Chakraverti AK, Kumar M (2024) De-noising the image using DBST-LCM-CLAHE: a deep learning approach. Multimed Tools Appl 83(4):11017\u201311042","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"8535_CR53","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/s10462-024-11022-8","volume":"58","author":"M Braik","year":"2024","unstructured":"Braik M, Al-Betar MA, Mahdi MA, Al-Shalabi M, Ahamad S, Saad SA (2024) Enhancement of satellite images based on CLAHE and augmented elk herd optimizer. Artif Intell Rev 58(2):38","journal-title":"Artif Intell Rev"},{"key":"8535_CR54","doi-asserted-by":"crossref","unstructured":"Zheng L, Shi H, Sun S (2016) Underwater image enhancement algorithm based on CLAHE and USM. In: 2016 IEEE International Conference on Information and Automation (ICIA), pp. 585\u2013590. IEEE","DOI":"10.1109\/ICInfA.2016.7831889"},{"key":"8535_CR55","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.compeleceng.2017.09.012","volume":"66","author":"G Cao","year":"2018","unstructured":"Cao G, Huang L, Tian H, Huang X, Wang Y, Zhi R (2018) Contrast enhancement of brightness-distorted images by improved adaptive gamma correction. Comput Electr Eng 66:569\u2013582","journal-title":"Comput Electr Eng"},{"issue":"18","key":"8535_CR56","first-page":"3681","volume":"118","author":"S Perumal","year":"2018","unstructured":"Perumal S, Velmurugan T (2018) Preprocessing by contrast enhancement techniques for medical images. Int J Pure Appl Math 118(18):3681\u20133688","journal-title":"Int J Pure Appl Math"},{"issue":"12","key":"8535_CR57","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1049\/iet-ipr.2019.0346","volume":"13","author":"APSS Raj","year":"2019","unstructured":"Raj APSS, Vajravelu SK (2019) DDLA: dual deep learning architecture for classification of plant species. IET Image Proc 13(12):2176\u20132182","journal-title":"IET Image Proc"},{"key":"8535_CR58","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556"},{"key":"8535_CR59","doi-asserted-by":"publisher","first-page":"156792","DOI":"10.1109\/ACCESS.2020.3019484","volume":"8","author":"W Xin","year":"2020","unstructured":"Xin W, Haojun X, Wei X, Qiang W, Zhang W, Han X (2020) Damage identification of low emissivity coating based on convolution neural network. IEEE Access 8:156792\u2013156800","journal-title":"IEEE Access"},{"key":"8535_CR60","unstructured":"Howard Andrew\u00a0G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"8535_CR61","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"8535_CR62","unstructured":"Krizhevsky A, Sutskever I, Hinton Geoffrey\u00a0E (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25"},{"issue":"5","key":"8535_CR63","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.13031\/trans.12440","volume":"61","author":"Z Lin","year":"2018","unstructured":"Lin Z, Shaomin M, Shi A, Pang C, Sun X et al (2018) A novel method of maize leaf disease image identification based on a multichannel convolutional neural network. Trans ASABE 61(5):1461\u20131474","journal-title":"Trans ASABE"},{"key":"8535_CR64","unstructured":"Kingma Diederik\u00a0P, Ba J (2014) Adam: a method for stochastic optimization. CoRR, arXiv:1412.6980"},{"issue":"10","key":"8535_CR65","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1080\/03610918.2014.931971","volume":"44","author":"DG Pereira","year":"2015","unstructured":"Pereira DG, Afonso A, Medeiros FM (2015) Overview of Friedman\u2019s test and post-hoc analysis. Commun Stat-Simul Comput 44(10):2636\u20132653","journal-title":"Commun Stat-Simul Comput"},{"key":"8535_CR66","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.compmedimag.2019.05.001","volume":"75","author":"ZNK Swati","year":"2019","unstructured":"Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Jianfeng L (2019) Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph 75:34\u201346","journal-title":"Comput Med Imaging Graph"},{"issue":"6","key":"8535_CR67","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.3390\/app10061999","volume":"10","author":"MM Bad\u017ea","year":"2020","unstructured":"Bad\u017ea MM, Barjaktarovi\u0107 M\u010c (2020) Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 10(6):1999","journal-title":"Appl Sci"},{"key":"8535_CR68","doi-asserted-by":"crossref","unstructured":"Ismael Mustafa\u00a0R, Abdel-Qader I (2018) Brain tumor classification via statistical features and back-propagation neural network. In: 2018 IEEE International Conference on Electro\/Information Technology (EIT), pp. 0252\u20130257. IEEE","DOI":"10.1109\/EIT.2018.8500308"},{"key":"8535_CR69","doi-asserted-by":"publisher","first-page":"69215","DOI":"10.1109\/ACCESS.2019.2919122","volume":"7","author":"HH Sultan","year":"2019","unstructured":"Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215\u201369225","journal-title":"IEEE Access"},{"issue":"20","key":"8535_CR70","doi-asserted-by":"publisher","first-page":"14611","DOI":"10.1007\/s00521-021-05841-x","volume":"35","author":"M Wo\u017aniak","year":"2023","unstructured":"Wo\u017aniak M, Si\u0142ka J, Wieczorek M (2023) Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput Appl 35(20):14611\u201314626","journal-title":"Neural Comput Appl"},{"key":"8535_CR71","doi-asserted-by":"crossref","unstructured":"Pashaei A, Sajedi H, Jazayeri N (2018) Brain tumor classification via convolutional neural network and extreme learning machines. In: 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 314\u2013319. IEEE","DOI":"10.1109\/ICCKE.2018.8566571"},{"key":"8535_CR72","doi-asserted-by":"publisher","first-page":"101678","DOI":"10.1016\/j.bspc.2019.101678","volume":"57","author":"N Ghassemi","year":"2020","unstructured":"Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR image. Biomed Signal Process Control 57:101678","journal-title":"Biomed Signal Process Control"},{"key":"8535_CR73","doi-asserted-by":"publisher","first-page":"153316","DOI":"10.1109\/ACCESS.2021.3127881","volume":"9","author":"M Nazar","year":"2021","unstructured":"Nazar M, Alam MM, Yafi E, Su\u2019ud MM (2021) A systematic review of human-computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access 9:153316\u2013153348","journal-title":"IEEE Access"},{"key":"8535_CR74","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3266418","author":"B Subrato","year":"2023","unstructured":"Subrato B, Rubaiyat MM, Hossain PP (2023) A review on explainable artificial intelligence for healthcare: why, how, and when? IEEE Trans Artif Intell. https:\/\/doi.org\/10.1109\/TAI.2023.3266418","journal-title":"IEEE Trans Artif Intell"},{"key":"8535_CR75","doi-asserted-by":"crossref","unstructured":"Ribeiro Marco\u00a0T, Singh S, Guestrin C (2016) \u201cWhy should i trust you?\u201d explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"key":"8535_CR76","unstructured":"Lundberg Scott\u00a0M, Lee S-I (2017) A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, 30"},{"issue":"7","key":"8535_CR77","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach S, Binder A, Montavon G, Klauschen F, M\u00fcller K-R, Samek W (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7):e0130140","journal-title":"PLoS ONE"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08535-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08535-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08535-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:42:19Z","timestamp":1778690539000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08535-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,10]]},"references-count":77,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["8535"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08535-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,10]]},"assertion":[{"value":"26 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study does not contain any studies with human or animal subjects performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"408"}}