{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:02:44Z","timestamp":1755223364031,"version":"3.43.0"},"reference-count":28,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potential for misdiagnosis. Additionally, deep learning models for tumor classification often require large amounts of labeled data, which can be costly and time-consuming to obtain, especially in the medical domain.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Objective<\/jats:title>\n<jats:p>Our main aim is to enhance Meningioma Tumor Classification Accuracy.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Method<\/jats:title>\n<jats:p>This study proposes a multi-task learning (MTL) approach to enhance the accuracy of meningioma tumor classification while mitigating the need for excessive labeled data. The primary task involves classifying meningioma tumors based on MRI imaging data, while auxiliary tasks leverage patient demographic information, such as age and gender. By incorporating these additional data sources into the learning process, the proposed MTL framework leverages the interdependencies among multiple tasks to improve overall prediction accuracy. The study evaluates the performance of the MTL approach using a dataset of 2218 brain MRI images from 34 patients diagnosed with meningioma, obtained from the Mahdia Imaging Center in Hamadan, Iran.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Results<\/jats:title>\n<jats:p>Results demonstrate that the MTL model significantly outperforms single-task learning baselines, achieving 99.6%\u2009\u00b1\u20090.2 accuracy on the test data in 95% confidence interval.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec005\">\n<jats:title>Discussion<\/jats:title>\n<jats:p>This highlights the efficacy of the proposed approach in enhancing meningioma tumor classification and its potential for aiding clinical decision-making and personalized treatment planning.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec006\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Our proposed method can be used in computer-aided diagnosis systems.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0327782","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T17:32:35Z","timestamp":1754933555000},"page":"e0327782","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images"],"prefix":"10.1371","volume":"20","author":[{"given":"Zahra","family":"Mehrpouya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5824-9798","authenticated-orcid":true,"given":"Toktam","family":"Khatibi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdolazim","family":"Sedighipashaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"issue":"2","key":"pone.0327782.ref001","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1056\/NEJM200101113440207","article-title":"Brain tumors","volume":"344","author":"LM DeAngelis","year":"2001","journal-title":"N Engl J Med"},{"issue":"9354","key":"pone.0327782.ref002","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S0140-6736(03)12328-8","article-title":"Primary brain tumours in adults","volume":"361","author":"A Behin","year":"2003","journal-title":"Lancet"},{"issue":"1","key":"pone.0327782.ref003","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1002\/cncr.29015","article-title":"Trends in central nervous system tumor incidence relative to other common cancers in adults, adolescents, and children in the United States, 2000 to 2010","volume":"121","author":"HR Gittleman","year":"2015","journal-title":"Cancer"},{"issue":"3","key":"pone.0327782.ref004","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1093\/neuonc\/noy195","article-title":"Malignant primary brain and other central nervous system tumors diagnosed in Canada from 2009 to 2013","volume":"21","author":"CBTR founding affiliates","year":"2019","journal-title":"Neuro Oncol"},{"key":"pone.0327782.ref005","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/978-1-60761-166-0_14","article-title":"Primary central nervous system lymphoma","volume-title":"Primary Central Nervous System Tumors: Pathogenesis and Therapy","author":"ER Gerstner","year":"2011"},{"key":"pone.0327782.ref006","doi-asserted-by":"crossref","DOI":"10.1093\/neuonc\/noy143","article-title":"Imaging and diagnostic advances for intracranial meningiomas","volume":"21","author":"RY Huang","year":"2019","journal-title":"Neuro Oncol"},{"key":"pone.0327782.ref007","doi-asserted-by":"crossref","first-page":"69215","DOI":"10.1109\/ACCESS.2019.2919122","article-title":"Multi-Classification of Brain Tumor Images Using Deep Neural Network","volume":"7","author":"HH Sultan","year":"2019","journal-title":"IEEE Access"},{"key":"pone.0327782.ref008","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"G Litjens","year":"2017","journal-title":"Med Image Anal"},{"issue":"6","key":"pone.0327782.ref009","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","article-title":"The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary","volume":"131","author":"DN Louis","year":"2016","journal-title":"Acta Neuropathol"},{"key":"pone.0327782.ref010","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006"},{"issue":"22","key":"pone.0327782.ref011","doi-asserted-by":"crossref","first-page":"29705","DOI":"10.1007\/s11042-018-6463-x","article-title":"A brief review on multi-task learning","volume":"77","author":"K-H Thung","year":"2018","journal-title":"Multimed Tools Appl"},{"key":"pone.0327782.ref012","doi-asserted-by":"crossref","unstructured":"Vlachostergiou A, Tagaris A, Stafylopatis A, Kollias S. Multi-task learning for predicting Parkinson\u2019s disease based on medical imaging information. In: 2018 25th IEEE International Conference on Image Processing (ICIP), 2018.","DOI":"10.1109\/ICIP.2018.8451398"},{"key":"pone.0327782.ref013","doi-asserted-by":"crossref","first-page":"690244","DOI":"10.3389\/fonc.2021.690244","article-title":"A Deep Multi-Task Learning Framework for Brain Tumor Segmentation","volume":"11","author":"H Huang","year":"2021","journal-title":"Front Oncol"},{"key":"pone.0327782.ref014","first-page":"1","article-title":"Machine learning and deep learning approach for medical image analysis: diagnosis to detection","author":"M Rana","year":"2022","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"pone.0327782.ref015","doi-asserted-by":"crossref","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":"AS Lundervold","year":"2019","journal-title":"Z Med Phys"},{"issue":"7553","key":"pone.0327782.ref016","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Y LeCun","year":"2015","journal-title":"Nature"},{"issue":"1","key":"pone.0327782.ref017","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s12880-022-00793-7","article-title":"Transfer learning for medical image classification: a literature review","volume":"22","author":"HE Kim","year":"2022","journal-title":"BMC Med Imaging"},{"key":"pone.0327782.ref018","article-title":"A comparative study for glioma classification using deep convolutional neural networks","author":"H \u00d6zcan","year":"2021","journal-title":"Molecular Biology and Evolution"},{"key":"pone.0327782.ref019","doi-asserted-by":"crossref","first-page":"106597","DOI":"10.1016\/j.cmpb.2021.106597","article-title":"A computer-aided grading of glioma tumor using deep residual networks fusion","volume":"215","author":"PC Tripathi","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"pone.0327782.ref020","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3390\/axioms11010034","article-title":"Brain Tumor Classification Using Dense Efficient-Net","volume":"11","author":"DR Nayak","year":"2022","journal-title":"Axioms"},{"issue":"1","key":"pone.0327782.ref021","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/s41746-023-00811-0","article-title":"Self-supervised learning for medical image classification: a systematic review and implementation guidelines","volume":"6","author":"S-C Huang","year":"2023","journal-title":"NPJ Digit Med"},{"key":"pone.0327782.ref022","article-title":"Meningioma","volume-title":"StatPearls [Internet]","author":"AA Alruwaili","year":"2022"},{"key":"pone.0327782.ref023","doi-asserted-by":"crossref","unstructured":"Wang P, Patel VM, Hacihaliloglu I. Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided CNN. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV, 2018.","DOI":"10.1007\/978-3-030-00937-3_16"},{"issue":"16","key":"pone.0327782.ref024","doi-asserted-by":"crossref","first-page":"11599","DOI":"10.1007\/s00521-021-06149-6","article-title":"A new deep belief network-based multi-task learning for diagnosis of Alzheimer\u2019s disease","volume":"35","author":"N Zeng","year":"2021","journal-title":"Neural Comput & Applic"},{"issue":"6","key":"pone.0327782.ref025","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s11633-022-1392-6","article-title":"Multitask Learning with Multiscale Residual Attention for Brain Tumor Segmentation and Classification","volume":"20","author":"G Li","year":"2023","journal-title":"Mach Intell Res"},{"key":"pone.0327782.ref026","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"AG Howard","year":"2017","journal-title":"arXiv preprint"},{"key":"pone.0327782.ref027","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.","DOI":"10.1109\/CVPR.2017.243"},{"key":"pone.0327782.ref028","unstructured":"Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, 2019."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327782","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T17:32:41Z","timestamp":1754933561000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327782"}},"subtitle":[],"editor":[{"given":"Ali Mohammad","family":"Alqudah","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,8,11]]},"references-count":28,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8,11]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0327782","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,11]]}}}