Motor imagery classification using sparse nonnegative matrix factorization and convolutional neural networks

Chaudhary, Poonam; Varshney, Yash Vardhan; Srivastava, Gautam; Bhatia, Surbhi

doi:10.1007/s00521-022-07861-7

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Its ability to generalize across different MRI machines and imaging modalities remains to be thoroughly tested. Future work could focus on expanding the model’s adaptability to various imaging conditions, enhancing its robustness and applicability ( Chaudhary et al, 2024 ). The model primarily focuses on cross-sectional MRI data.…”

Section: Discussionmentioning

confidence: 99%

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach

Albalawi,

Thakur,

Dorai

et al. 2024

Front. Comput. Neurosci.

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BackgroundThe necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error.ObjectiveThis research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans.MethodsThe dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification.ResultsThe proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications.ConclusionThis study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

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Section: Discussionmentioning

confidence: 99%