{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T18:41:27Z","timestamp":1786214487962,"version":"3.56.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"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>With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.<\/jats:p>","DOI":"10.3389\/fncom.2022.1005617","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T05:30:36Z","timestamp":1662096636000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":97,"title":["Ensemble deep learning for brain tumor detection"],"prefix":"10.3389","volume":"16","author":[{"given":"Shtwai","family":"Alsubai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Habib Ullah","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alqahtani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohemmed","family":"Sha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sidra","family":"Abbas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uzma Ghulam","family":"Mohammad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"788347","DOI":"10.3389\/fpubh.2021.788347","article-title":"Trustworthy intrusion detection in e-healthcare systems","volume":"9","author":"Akram","year":"2021","journal-title":"Front. Public Health"},{"key":"B2","doi-asserted-by":"publisher","first-page":"372","DOI":"10.3390\/s22010372","article-title":"Brain tumor\/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model","volume":"22","author":"Alanazi","year":"2022","journal-title":"Sensors"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.873268","article-title":"A sequential machine learning-cum-attention mechanism for effective segmentation of brain tumor","author":"Ali","year":"2022","journal-title":"Front Oncol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"4297","DOI":"10.3390\/s22114297","article-title":"Braingan: brain mri image generation and classification framework using gan architectures and cnn models","volume":"22","author":"Alrashedy","year":"2022","journal-title":"Sensors"},{"key":"B5","doi-asserted-by":"publisher","first-page":"3773","DOI":"10.3390\/app12083773","article-title":"A novel data augmentation-based brain tumor detection using convolutional neural network","volume":"12","author":"Alsaif","year":"2022","journal-title":"Appl. Sci"},{"key":"B6","doi-asserted-by":"publisher","first-page":"77131","DOI":"10.1109\/ACCESS.2020.2989396","article-title":"Alzheimer\u0160s diseases detection by using deep learning algorithms: a mini-review","volume":"8","author":"Al-Shoukry","year":"2020","journal-title":"IEEE Access"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3236305","article-title":"A new model for brain tumor detection using ensemble transfer learning and quantum variational classifier","author":"Amin","year":"2022","journal-title":"Comput. Intell. Neurosci"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3161","DOI":"10.1007\/s40747-021-00563-y","article-title":"Brain tumor detection and classification using machine learning: a comprehensive survey","volume":"8","author":"Amin","year":"2021","journal-title":"Complex Intell. Syst"},{"key":"B9","doi-asserted-by":"publisher","first-page":"15965","DOI":"10.1007\/s00521-019-04650-7","article-title":"Brain tumor detection: a long short-term memory (lstm)-based learning model","volume":"32","author":"Amin","year":"2020","journal-title":"Neural Comput. Appl"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115805","article-title":"Covh2sd: A COVID-19 detection approach based on harris hawks optimization and stacked deep learning","author":"Balaha","year":"2021","journal-title":"Expert. Syst. Appl"},{"key":"B11","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/NILES50944.2020.9257930","article-title":"An artificial intelligence based technique for COVID-19 diagnosis from chest x-ray,","volume-title":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","author":"Bekhet","year":"2020"},{"key":"B12","year":"2022","journal-title":"Cancer"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1610.02583","article-title":"A gentle tutorial of recurrent neural network with error backpropagation","author":"Chen","year":"2016","journal-title":"arXiv preprint arXiv:1610.02583"},{"key":"B14","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/ICCMC.2019.8819782","article-title":"Designing disease prediction model using machine learning approach,","volume-title":"2019 3rd International Conference on Computing Methodologies and Communication (ICCMC)","author":"Dahiwade","year":"2019"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/2483285","article-title":"Automatic detection of brain tumor on computed tomography images for patients in the intensive care unit","author":"Fahmi","year":"2020","journal-title":"J. Healthc Eng"},{"key":"B16","doi-asserted-by":"publisher","first-page":"2343","DOI":"10.3390\/diagnostics11122343","article-title":"Classification of brain mri tumor images based on deep learning pggan augmentation","volume":"11","author":"Gab Allah","year":"2021","journal-title":"Diagnostics"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-31760-7_1","article-title":"Learning activation functions: A new paradigm for understanding neural networks","author":"Goyal","year":"2019","journal-title":"arXiv preprint arXiv:1906.09529"},{"key":"B18","first-page":"645","article-title":"Integration of internet of things and cloud computing for cardiac health recognition,","volume-title":"Metaheuristics in Machine Learning: Theory and Applications","author":"Houssein","year":""},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115131","article-title":"An efficient ecg arrhythmia classification method based on manta ray foraging optimization","author":"Houssein","year":"","journal-title":"Expert. Syst. Appl"},{"key":"B20","doi-asserted-by":"publisher","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","article-title":"A combined deep cnn-lstm network for the detection of novel coronavirus (COVID-19) using x-ray images","volume":"20","author":"Islam","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102572","article-title":"Automated cognitive health assessment in smart homes using machine learning","author":"Javed","year":"","journal-title":"Sustain. Cities Soc"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13673-020-00245-7","article-title":"A collaborative healthcare framework for shared healthcare plan with ambient intelligence","volume":"10","author":"Javed","year":"2020","journal-title":"Human Centric Comput. Inf. Sci"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11063-020-10414-5","article-title":"Pp-spa: privacy preserved smartphone-based personal assistant to improve routine life functioning of cognitive impaired individuals","author":"Javed","year":"","journal-title":"Neural Process. Lett"},{"key":"B24","doi-asserted-by":"publisher","first-page":"103794","DOI":"10.1016\/j.cities.2022.103794","article-title":"Future smart cities requirements, emerging technologies, applications, challenges, and future aspects","volume":"129","author":"Javed","year":"2022","journal-title":"Cities"},{"key":"B25","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.3390\/s21062222","article-title":"Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers","volume":"21","author":"Kang","year":"2021","journal-title":"Sensors"},{"key":"B26","doi-asserted-by":"publisher","first-page":"2867","DOI":"10.1007\/s12652-020-02444-7","article-title":"Mri brain tumor detection using optimal possibilistic fuzzy c-means clustering algorithm and adaptive k-nearest neighbor classifier","volume":"12","author":"Kumar","year":"2021","journal-title":"J. Ambient. Intell. Humaniz Comput"},{"key":"B27","doi-asserted-by":"publisher","first-page":"103440","DOI":"10.1016\/j.bspc.2021.103440","article-title":"An approach for brain tumor detection using optimal feature selection and optimized deep belief network","volume":"73","author":"Kumar","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/fcell.2021.765654","article-title":"PBTNet: a new computer-aided diagnosis system for detecting primary brain tumors","author":"Lu","year":"2021","journal-title":"Front. Cell Dev. Biol"},{"key":"B29","doi-asserted-by":"publisher","first-page":"788376","DOI":"10.3389\/fpubh.2021.788376","article-title":"Machine learning assisted cervical cancer detection","volume":"9","author":"Mehmood","year":"2021","journal-title":"Front. Public Health"},{"key":"B30","doi-asserted-by":"publisher","first-page":"4747","DOI":"10.3390\/s20174747","article-title":"Spectral-spatial features integrated convolution neural network for breast cancer classification","volume":"20","author":"Mewada","year":"2020","journal-title":"Sensors"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1359019","article-title":"Breast tumor detection and classification in mammogram images using modified yolov5 network","author":"Mohiyuddin","year":"2022","journal-title":"Comput. Math. Methods Med"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1142\/S0218126622500104","article-title":"Storage and proximity management for centralized personal health records using an ipfs-based optimization algorithm","author":"Mubashar","year":"2022","journal-title":"J. Circ. Syst. Comput"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3390\/app12083715","article-title":"Intelligent ultra-light deep learning model for multi-class brain tumor detection","author":"Qureshi","year":"2022","journal-title":"Appl. Sci"},{"key":"B34","unstructured":"Brain tumor detection using deep neural network and machine learning algorithm10851093\n            RathodR.\n            KhanR. A. H.\n          PalArchs J. Archaeol. Egypt Egyptol182021"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3171852","article-title":"Federated learning for privacy preservation of healthcare data from smartphone-based side-channel attacks","author":"Rehman","year":"","journal-title":"IEEE J. Biomed. Health Informa"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1080\/17517575.2020.1852316","article-title":"Personalisedcomfort: a personalised thermal comfort model to predict thermal sensation votes for smart building residents","author":"Rehman","year":"","journal-title":"Enterprise Inf. Syst"},{"key":"B37","doi-asserted-by":"publisher","first-page":"109705","DOI":"10.1016\/j.mehy.2020.109705","article-title":"Texture based localization of a brain tumor from mr-images by using a machine learning approach","volume":"141","author":"Rehman","year":"2020","journal-title":"Med. Hypotheses"},{"key":"B38","doi-asserted-by":"publisher","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":"Rizwan","year":"","journal-title":"IEEE Access"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107833","article-title":"Risk monitoring strategy for confidentiality of healthcare information","author":"Rizwan","year":"","journal-title":"Comput. Electr. Eng"},{"key":"B40","doi-asserted-by":"publisher","first-page":"16441","DOI":"10.1007\/s11042-022-12362-9","article-title":"A novel framework for brain tumor detection based on convolutional variational generative models","volume":"81","author":"Salama","year":"2022","journal-title":"Multimed. Tools Appl"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8330833","article-title":"Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning","author":"Senan","year":"2022","journal-title":"Comput. Math. Methods Med"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1830010","article-title":"Cnn based multiclass brain tumor detection using medical imaging","author":"Tiwari","year":"2022","journal-title":"Comput. Intell. Neurosci"},{"key":"B43","doi-asserted-by":"crossref","DOI":"10.1007\/s11548-022-02619-x","article-title":"Explainability of deep neural networks for mri analysis of brain tumors,","author":"Zeineldin","year":"2022","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8996673","article-title":"Optimization of tumor disease monitoring in medical big data environment based on high-order simulated annealing neural network algorithm","author":"Zhang","year":"2021","journal-title":"Comput. Intell. Neurosci"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.3174\/ajnr.A6621","article-title":"Automatic machine learning to differentiate pediatric posterior fossa tumors on routine mr imaging","volume":"41","author":"Zhou","year":"2020","journal-title":"Am. J. Neuroradiol"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2022.1005617\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T05:30:43Z","timestamp":1662096643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2022.1005617\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,2]]},"references-count":45,"alternative-id":["10.3389\/fncom.2022.1005617"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2022.1005617","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,2]]},"article-number":"1005617"}}