{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:30:49Z","timestamp":1783020649626,"version":"3.54.6"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research and Libraries, Princess Nourah bint Abdulrahman University","award":["RPFAP-121-1445"],"award-info":[{"award-number":["RPFAP-121-1445"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Brain tumors are one of the most fatal disorders that cause one of the highest mortalities in the world. Gliomas are the most common primary brain tumors originating from glial cells in the central nervous system. Traditionally, a tissue sample is extracted and examined for its genetic and characteristic properties. This method is invasive, painful, and takes a longer period to produce results. Various automatic Deep learning (DL) based schemes have been presented for the brain glioma detection, but they lack due to poor explainability, lower generalization, poor feature depiction, class imbalance problem and lower detection rate. This paper presents a deep learning based brain tumor detection using two way feature depiction model (TWFDM) that combines the 2D-Deep Convolution Neural Network (DCNN) and 1D-DCNN. The 2D-DCNN accepts the raw MRI images and the 1D-DCNN accepts the handcrafted local binary pattern (LBP), gray level cooccurrence matrix (GLCM), and Histogram of Oriented Gradient (HOG) features. Furthermore, improved particle swarm optimization (IPSO) is used for feature selection to minimize the computational complexity of the TWFDM system. The proposed TWFDM achieves an overall accuracy of 96.25%, a recall of 96.34%, a precision of 96.31%, and an F1-score of 96.32% on the Brain MRI dataset for four-class classification, representing an important improvement over traditional techniques.<\/jats:p>","DOI":"10.1371\/journal.pone.0344291","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:10:53Z","timestamp":1783015853000},"page":"e0344291","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning based two-way feature depiction model for brain tumor detection"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1477-8759","authenticated-orcid":true,"given":"Shabana","family":"Urooj","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0740-7941","authenticated-orcid":true,"given":"Kiran","family":"Napte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6381-9019","authenticated-orcid":true,"given":"Najah","family":"Alsubaie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"issue":"1","key":"pone.0344291.ref001","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3390\/jimaging11010002","article-title":"The neural frontier of future medical imaging: a review of deep learning for brain tumor detection","volume":"11","author":"T Berghout","year":"2024","journal-title":"J Imaging"},{"issue":"25","key":"pone.0344291.ref002","doi-asserted-by":"crossref","first-page":"65889","DOI":"10.1007\/s11042-024-18129-8","article-title":"Deep learning for multi-grade brain tumor detection and classification: a prospective survey","volume":"83","author":"K Bhagyalaxmi","year":"2024","journal-title":"Multimedia Tools and Applications"},{"issue":"3","key":"pone.0344291.ref003","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1007\/s10278-024-01283-8","article-title":"Deep learning approaches for brain tumor detection and classification using MRI images (2020 to 2024): a systematic review","volume":"38","author":"S Bouhafra","year":"2025","journal-title":"J Imaging Inform Med"},{"issue":"1","key":"pone.0344291.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11517-023-02873-4","article-title":"A survey on brain tumor image analysis","volume":"62","author":"K Sailunaz","year":"2024","journal-title":"Med Biol Eng Comput"},{"issue":"13","key":"pone.0344291.ref005","doi-asserted-by":"crossref","first-page":"11551","DOI":"10.1007\/s11042-024-19333-2","article-title":"Brain tumour detection using machine and deep learning: a systematic review","volume":"84","author":"N Rasool","year":"2025","journal-title":"Multimedia tools and applications"},{"issue":"1","key":"pone.0344291.ref006","doi-asserted-by":"crossref","first-page":"01","DOI":"10.38094\/jastt61188","article-title":"Classification of brain tumor based on machine learning algorithms: a review","volume":"6","author":"O Azeez","year":"2024","journal-title":"JASTT"},{"key":"pone.0344291.ref007","doi-asserted-by":"crossref","first-page":"101576","DOI":"10.1016\/j.imu.2024.101576","article-title":"Advancements in deep learning techniques for brain tumor segmentation: a survey","volume":"50","author":"CM Umarani","year":"2024","journal-title":"Informatics in Medicine Unlocked"},{"key":"pone.0344291.ref008","doi-asserted-by":"crossref","first-page":"104859","DOI":"10.1016\/j.bspc.2023.104859","article-title":"Glioma brain tumor detection using dual convolutional neural networks and histogram density segmentation algorithm","volume":"85","author":"B Sarala","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"issue":"1","key":"pone.0344291.ref009","first-page":"4380901","article-title":"Computational and mathematical methods in medicine glioma brain tumor detection and classification using convolutional neural network","volume":"2022","author":"S Saravanan","year":"2022","journal-title":"Computational and Mathematical Methods in Medicine"},{"issue":"9","key":"pone.0344291.ref010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0291200","article-title":"TumorDetNet: a unified deep learning model for brain tumor detection and classification","volume":"18","author":"N Ullah","year":"2023","journal-title":"PLoS One"},{"issue":"16","key":"pone.0344291.ref011","doi-asserted-by":"crossref","first-page":"4172","DOI":"10.3390\/cancers15164172","article-title":"Brain tumor detection based on deep learning approaches and magnetic resonance imaging","volume":"15","author":"AB Abdusalomov","year":"2023","journal-title":"Cancers (Basel)"},{"key":"pone.0344291.ref012","first-page":"409","article-title":"Enhancing diagnosis: An ensemble deep learning model for brain tumor detection and classification.","volume-title":"International Conference on Big Data, IoT and Machine Learning","author":"T Mahmud","year":"2023"},{"issue":"1","key":"pone.0344291.ref013","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.1038\/s41598-024-57970-7","article-title":"Employing deep learning and transfer learning for accurate brain tumor detection","volume":"14","author":"SK Mathivanan","year":"2024","journal-title":"Sci Rep"},{"key":"pone.0344291.ref014","doi-asserted-by":"crossref","first-page":"29731","DOI":"10.1109\/ACCESS.2022.3153108","article-title":"Brain tumor and glioma grade classification using gaussian convolutional neural network","volume":"10","author":"M Rizwan","year":"2022","journal-title":"IEEE Access"},{"issue":"3","key":"pone.0344291.ref015","doi-asserted-by":"crossref","first-page":"266","DOI":"10.3390\/bioengineering11030266","article-title":"Brain tumor detection and categorization with segmentation of improved unsupervised clustering approach and machine learning classifier","volume":"11","author":"U Bhimavarapu","year":"2024","journal-title":"Bioengineering (Basel)"},{"issue":"24","key":"pone.0344291.ref016","doi-asserted-by":"crossref","first-page":"4178","DOI":"10.3390\/electronics11244178","article-title":"Handcrafted deep-feature-based brain tumor detection and classification using mri images","volume":"11","author":"P Mohan","year":"2022","journal-title":"Electronics"},{"key":"pone.0344291.ref017","doi-asserted-by":"crossref","first-page":"4733","DOI":"10.1016\/j.csbj.2022.08.039","article-title":"Accurate brain tumor detection using deep convolutional neural network","volume":"20","author":"MSI Khan","year":"2022","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"pone.0344291.ref018","doi-asserted-by":"crossref","first-page":"708","DOI":"10.59543\/comdem.v2i.14243","article-title":"Breakthroughs in brain tumor detection: leveraging deep learning and transfer learning for MRI-based classification","volume":"2","author":"A Golkarieh","year":"2025","journal-title":"COMDEM"},{"issue":"1","key":"pone.0344291.ref019","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12911-023-02114-6","article-title":"MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques","volume":"23","author":"S Saeedi","year":"2023","journal-title":"BMC Med Inform Decis Mak"},{"key":"pone.0344291.ref020","first-page":"100694","article-title":"MRI brain tumor detection and classification using parallel deep convolutional neural networks","volume":"26","author":"T Rahman","year":"2023","journal-title":"Measurement: Sensors"},{"issue":"8","key":"pone.0344291.ref021","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.3390\/app12083715","article-title":"Intelligent ultra-light deep learning model for multi-class brain tumor detection","volume":"12","author":"SA Qureshi","year":"2022","journal-title":"Applied Sciences"},{"issue":"7","key":"pone.0344291.ref022","first-page":"1449","article-title":"Brain tumor detection and classification using fine-tuned CNN with ResNet50 and U-Net model: a study on TCGA-LGG and TCIA dataset for MRI applications","volume":"13","author":"AA Asiri","year":"2023","journal-title":"Life (Basel)"},{"issue":"11","key":"pone.0344291.ref023","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.3390\/app12115645","article-title":"An effective approach to detect and identify brain tumors using transfer learning","volume":"12","author":"N Ullah","year":"2022","journal-title":"Applied Sciences"},{"key":"pone.0344291.ref024","doi-asserted-by":"crossref","first-page":"105716","DOI":"10.1016\/j.bspc.2023.105716","article-title":"Brain tumor classification and detection via hybrid alexnet-gru based on deep learning","volume":"89","author":"A Priya","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"issue":"4","key":"pone.0344291.ref025","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s42044-023-00139-8","article-title":"Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model","volume":"6","author":"M Yildirim","year":"2023","journal-title":"Iran Journal of Computer Science"},{"key":"pone.0344291.ref026","first-page":"386","article-title":"Glioma brain tumor segmentation in four MRI modalities using a convolutional neural network and based on a transfer learning method.","volume-title":"Brazilian technology symposium","author":"N Sarshar Tataei","year":"2021"},{"key":"pone.0344291.ref027","doi-asserted-by":"crossref","first-page":"100383","DOI":"10.1016\/j.mtbio.2022.100383","article-title":"Biomimetic semiconducting polymer dots for highly specific NIR-II fluorescence imaging of glioma","volume":"16","author":"X Men","year":"2022","journal-title":"Mater Today Bio"},{"key":"pone.0344291.ref028","doi-asserted-by":"crossref","first-page":"105539","DOI":"10.1016\/j.compbiomed.2022.105539","article-title":"A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models","volume":"146","author":"NF Aurna","year":"2022","journal-title":"Comput Biol Med"},{"key":"pone.0344291.ref029","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/9781394214259.ch9","article-title":"Computational intelligence and deep learning in health informatics: an introductory perspective","author":"J Naskath","year":"2024","journal-title":"Computational Intelligence: Theory and Applications"},{"issue":"4","key":"pone.0344291.ref030","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1007\/s11277-022-10079-4","article-title":"A study on different deep learning algorithms used in deep neural nets: MLP SOM and DBN","volume":"128","author":"J Naskath","year":"2023","journal-title":"Wireless personal communications"},{"key":"pone.0344291.ref031","unstructured":"Brain tumor MRI dataset. 2025. https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0344291","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:11:07Z","timestamp":1783015867000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0344291"}},"subtitle":[],"editor":[{"given":"Ananth","family":"JP","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":31,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0344291","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]}}}