Deep Learning Researcher (PhD Collaboration)

• Conducted innovative research in deep learning for medical imaging, achieving a 2.5% accuracy improvement

over benchmark models for brain tumor segmentation.

• We developed different models for segmenting brain tumors from MRI images.

• Designed, implemented, and fine-tuned advanced architectures like U-Net and U-Net++, attaining 99.86%

accuracy on MRI datasets.

• We evaluated our model with different evaluation metrics (DOC, F1-score, ROC, etc.).

• Authored a research paper comparing the model's superior performance metrics (DOC, F1-score, ROC) against

related works.