Comprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation.

Area of Science:

• Medical Image Analysis
• Artificial Intelligence in Oncology
• Neuroimaging

Background:

• Accurate brain tumor identification and segmentation from MRI images are crucial for diagnosis and treatment planning.
• Deep learning models have demonstrated significant potential in medical image analysis, but often with high computational costs.
• Existing methods for brain tumor segmentation and classification face challenges in balancing performance with computational complexity.

Purpose of the Study:

• To investigate the efficacy of popular deep learning architectures (Xception Net, MobileNet, DeepLab) for brain tumor classification and segmentation.
• To evaluate the performance of these models on the BRATS 2018 dataset for accurate tumor detection and boundary delineation.
• To assess the trade-offs between performance and computational complexity in deep learning-based brain tumor analysis.

Main Methods:

• Utilized deep learning architectures: Xception Net and MobileNet for classification, and DeepLab for segmentation.
• Trained and evaluated models using the BRATS 2018 dataset, a standard benchmark for brain tumor image analysis.
• Assessed classification accuracy and segmentation precision using metrics such as Pearson Correlation Coefficient.

Main Results:

• DeepLab models achieved a segmentation performance with a Pearson Correlation Coefficient of 0.50.
• Xception Net and MobileNet models demonstrated high classification accuracy, achieving 0.8921 and 0.9176, respectively.
• The experimental results indicate that these architectures offer both high accuracy and precise segmentation for brain tumors.

Conclusions:

• The investigated deep learning architectures (Xception Net, MobileNet, DeepLab) are effective for brain tumor classification and segmentation from MRI data.
• These models provide a promising approach for improving the accuracy and efficiency of brain tumor analysis in clinical settings.
• The findings contribute to the advancement of medical image analysis, potentially leading to better diagnosis and management of brain tumors.