عرض بسيط للتسجيلة

المؤلفAlsubai, Shtwai
المؤلفKhan, Habib Ullah
المؤلفAlqahtani, Abdullah
المؤلفSha, Mohemmed
المؤلفAbbas, Sidra
المؤلفMohammad, Uzma Ghulam
تاريخ الإتاحة2022-12-26T08:00:18Z
تاريخ النشر2022-09-02
اسم المنشورFrontiers in Computational Neuroscience
المعرّفhttp://dx.doi.org/10.3389/fncom.2022.1005617
الاقتباسAlsubai, 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.
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138169673&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37581
الملخصWith 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%.
راعي المشروعQatar National Library and Qatar university internal - grant No. IRCC-2021-010
اللغةen
الناشرFrontiers
الموضوعbrain tumor
CNN-LSTM
convolutional neural network
deep learning
long short-term memory
MR images
العنوانEnsemble deep learning for brain tumor detection
النوعArticle
رقم المجلد16
ESSN1662-5188
dc.accessType Open Access


الملفات في هذه التسجيلة

Thumbnail

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة