The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

Area of Science:

• Biomedical image analysis
• Artificial intelligence in oncology
• Medical imaging informatics

Background:

• Deep learning techniques, particularly Deep Convolutional Neural Networks (DCNNs), are increasingly utilized in biomedical image processing.
• DCNNs offer advanced capabilities for analyzing complex medical images, including magnetic resonance imaging (MRI) and histopathological imaging (H&E).
• These networks can automatically extract intricate features from images, assisting in tasks that are challenging for human experts.

Purpose of the Study:

• To summarize recent advancements in deep learning for brain cancer image analysis.
• To highlight the application of DCNNs in classifying and segmenting brain tumors.
• To discuss challenges and future directions for DCNNs in personalized brain cancer care.

Main Methods:

• Review of recent studies applying deep learning techniques to brain cancer medical images.
• Focus on DCNN architectures for feature extraction in image-based data.
• Analysis of DCNN applications in tumor classification and segmentation using histology, MRI, and computed tomography (CT) data.

Main Results:

• DCNNs demonstrate significant potential in classifying and segmenting brain tumors across various imaging modalities.
• The multi-layered feature extraction of DCNNs surpasses traditional manual analysis in speed and detail.
• Studies reviewed show promising results for DCNNs in differentiating tumor types and delineating tumor boundaries.

Conclusions:

• Deep learning, especially DCNNs, is a powerful tool for brain tumor image analysis, classification, and segmentation.
• The application of DCNNs holds promise for advancing personalized brain cancer diagnosis and treatment planning.
• Further research is needed to address current challenges for broader clinical applicability of DCNNs in oncology.