Measure the superior functionality of machine intelligence in brain tumor disease prediction

Yadav, Dhyan Chandra; Pal, Saurabh

doi:10.1016/b978-0-323-91197-9.00005-9

Cited by 2 publications

(2 citation statements)

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“…Hybrid models combining CNNs for feature extraction and RNNs for sequential analysis have demonstrated improved performance, showcasing the synergistic benefits of combining different deep learning paradigms. D. C. Yadav and S. Pal [11] concluded that despite the remarkable progress, several challenges persist in the application of deep learning techniques to brain tumor pre-diction. Variability in tumor types, sizes, and locations poses a significant challenge for model generalization.…”

Section: Literature Reviewmentioning

confidence: 99%

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Neural Networks in Neuroimaging: A Critical Analysis of Deep Learning Techniques for Brain Tumor Prediction

S. Muthuraj,

2024

jes

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With the escalating incidence of brain tumors, early and accurate detection holds paramount importance for facilitating timely medical interventions and enhancing patient outcomes. Deep learning models have proven to be formidable tools for analyzing intricate medical data, particularly in the context of medical imaging. This review encompasses a meticulous examination of various deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, as they are employed across diverse medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). The paper delves into the distinctive challenges inherent in brain tumor prediction, acknowledging factors such as inherent tumor variability and the necessity for models that are interpretable and applicable within clinical settings. Additionally, the review explores the fusion of multimodal data and the integration of transfer learning and domain adaptation techniques to enhance model generalization across diverse datasets. A critical evaluation of the strengths and limitations of current methodologies is provided, offering insights into potential avenues for future research. Moreover, this paper provides an extensive overview of deep learning methodologies applied to the prediction of brain tumors, exploring recent advancements and confronting challenges in this pivotal domain of medical research

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Section: Literature Reviewmentioning

confidence: 99%

“…A certified clinical neuropsychologist is the one who administers these exams. This professional will compile their findings into a formal report that may be compared to subsequent exams or used to zero in on certain issues that can be remedied through treatment [11,16,17].…”

Section: F Neurocognitive Evolutionmentioning

confidence: 99%

Neural Networks in Neuroimaging: A Critical Analysis of Deep Learning Techniques for Brain Tumor Prediction

S. Muthuraj,

2024

jes

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Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms

Yadav

Singh

Pal

2023

Biomedical Materials & Devices

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Measure the superior functionality of machine intelligence in brain tumor disease prediction

Cited by 2 publications

References 15 publications

Neural Networks in Neuroimaging: A Critical Analysis of Deep Learning Techniques for Brain Tumor Prediction

Neural Networks in Neuroimaging: A Critical Analysis of Deep Learning Techniques for Brain Tumor Prediction

Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms

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