Brain Tumor Segmentation

• Tech Stack: Python, Tensorflow, Matplotlib
• Publication URL: Link

Brain tumor segmentation can be used to separate healthy tissues from cancerous tissues accurately. Usually, in clinical settings, a brain MRI (Magnetic Resonance Imaging) scan is used to detect tumors, but manually segmenting out a tumor is a time-consuming process and requires highly skilled and experienced radiologists. If a malignant tumor is not detected in the early stages of cancer, it can cost a life. Hence, early detection of tumors in a short amount of time would be a great boon to humanity.

This paper proposes a brain tumor segmentation technique using a Deep Convolutional Neural Network (CNN). The type of Convolutional Neural Network proposed is called U-NET. The U-NET architecture uses semantic segmentation that labels every pixel of an image rather than just detecting objects. So, if we can segment out an image, we would know which pixel belongs to what part of a human’s anatomy. It can help us efficiently detect all types of tumors and irregularities, which would help the radiologists and surgeons with detection and surgery.