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AuthorAlsubai, Shtwai
AuthorKhan, Habib Ullah
AuthorAlqahtani, Abdullah
AuthorSha, Mohemmed
AuthorAbbas, Sidra
AuthorMohammad, Uzma Ghulam
Available date2022-12-26T08:00:18Z
Publication Date2022-09-02
Publication NameFrontiers in Computational Neuroscience
Identifierhttp://dx.doi.org/10.3389/fncom.2022.1005617
CitationAlsubai, S., Khan, H. U., Alqahtani, A., Sha, M., Abbas, S., & Mohammad, U. G. (2022). Ensemble deep learning for brain tumor detection. Frontiers in Computational Neuroscience, 16, 1005617-1005617.
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138169673&origin=inward
URIhttp://hdl.handle.net/10576/37581
AbstractWith 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%.
SponsorQatar National Library and Qatar university internal - grant No. IRCC-2021-010
Languageen
PublisherFrontiers
Subjectbrain tumor
CNN-LSTM
convolutional neural network
deep learning
long short-term memory
MR images
TitleEnsemble deep learning for brain tumor detection
TypeArticle
Volume Number16
ESSN1662-5188
dc.accessType Open Access


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