Artificial Intelligence in Brain Tumors.

Area of Science:

• Neuroscience
• Computer Science
• Medical Imaging

Background:

• Artificial intelligence (AI), encompassing machine learning and deep learning, has evolved significantly since its origins in the 1940s.
• Deep learning models, particularly convolutional and recurrent networks, excel at analyzing complex data structures in images, video, audio, and sequential data.
• Radiomics extracts quantitative features from medical images, enabling predictive analysis when combined with machine learning algorithms.

Purpose of the Study:

• To review the applications of AI methodologies in the field of brain tumors.
• To emphasize the importance of data preprocessing and augmentation in AI-driven medical research.
• To explore the use of deep learning for brain tumor segmentation and data fusion.

Main Methods:

• Review of artificial intelligence methodologies, including deep learning, machine learning, and radiomics.
• Discussion of data preprocessing and augmentation techniques relevant to medical imaging.
• Exploration of deep learning models for brain tumor segmentation and integration of clinical and imaging data.

Main Results:

• Deep learning models have demonstrated significant advancements in analyzing complex datasets and improving data representation.
• AI and radiomics show promise in making interesting predictions when applied to brain tumor data.
• The fusion of clinical and imaging data using AI can provide deeper insights into brain tumors.

Conclusions:

• AI methodologies, especially deep learning, offer powerful tools for brain tumor research.
• Effective data preprocessing and augmentation are crucial for successful AI applications in this domain.
• Deep learning models are valuable for brain tumor segmentation and integrating diverse data sources.