🎯 The Objective
Multiple Sclerosis (MS) is a rare neurological disease where early diagnosis is critical. The goal was to automate the segmentation of lesions in brain MRI scans to enhance diagnostic precision. Figure 1: MS Lesion Segmentation Mask.
🛠️ Tech Stack
- Core: Python, TensorFlow, Keras
- Imaging: OpenCV, PyDicom, PIL
- Models: U-Net with ResNet/DenseNet backbones
🔬 Methodology
I performed a comprehensive evaluation of various U-Net combinations. By integrating deep feature extraction backbones (specifically ResNet and DenseNet), the model could capture finer contextual details in the MRI scans compared to standard architectures.
📊 Impact
The optimal configuration identified in this study demonstrated improved segmentation accuracy, potentially aiding clinicians in earlier diagnosis and treatment planning for MS patients.