Interpretation of Brain Tumour Using Deep Learning Model

Avanija, J.; Ramji, Banothu; Prabhu, Arpan V.; Maheswari, K.; Vittal, R. Hitesh Sai; Jagannadham, D. B. V.; Kumar, Voruganti Naresh

doi:10.1007/978-981-19-8563-8_33

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, manual classification methods may lack scalability and generalizability, as they may not effectively handle large and complex datasets. Overall, manual feature selection and classification methods are less efficient and may not fully capture the complexity of brain states compared to machine learning techniques, which can automatically learn and adapt to patterns in the data, leading to more robust and accurate classifications [18] [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Gnanajeyaraman Rajaram

2024

jes

View full text Add to dashboard Cite

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

show abstract

Section: Literature Reviewmentioning

confidence: 99%