Classifying Brain Tumor Types by MRI Scans with Convolutional Neural Network

Jul 29, 2021 · Chicago, United States of America

The brain tumor is known to be one of the most aggressive and fatal diseases. It is possible to have a more cost-effective way to initially triage different types of brain tumors to get timely treatments before the brain tumor grows and becomes untreatable in the later stage. By helping the initial triage as soon as an anomaly is detected, the medical staff can prioritize those patients potentially with malignant tumors faster and in a highly efficient way. The background of this data set is that MRI scans are taken from medical professionals from different angles.

Convolutional Neural Networks for multi-classes classification used in this project is a deep learning technique with high potentials to be tuned for increased accuracy. In addition, other methods like using Keras Tuner is explored to pick the optimal set of hyperparameters.

Angela Lau is an experienced product professional and entrepreneur. She constantly thinks about using data science and machine learning to build products that solve real-world problems and delight users. As a builder at heart and engineer by training, Angela loves to roll up her sleeves and be hands-on to solve problems. She was a former startup founder, software engineer, product manager at major technology companies, such as Apple and Oracle. She is currently leading a computer vision AI project for occupational ergonomic assessments at SwiftMotion.io.

Angela graduated from the University of Southern California’s Viterbi School of Engineering. She also holds an MBA degree at the Haas School of Business, University of California Berkeley.

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