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Brain Tumor Detection using Convolutional Neural Network
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Part of the ECE 542 Virtual Symposium (Spring 2020)
In order to improve human judgement in diagnosis advent of new technology into health care can be witnessed. An entrance of computer vision into diagnosis would reduce human error in judgment. For example, examining MRI results for brain related ailments requires extremely high concentration and wrong judgement would be catastrophic. The MRI scans are capable of identifying even the smallest aberrations in the human body. Here we train a model to specifically identify these tiny aberrations from MRIs and predict presence of a tumor with high accuracy. Convolutional Neural Network (CNN) is one of the most effective techniques for this problem statement. Thus using image preprocessing and transfer learning using VGG16, we built a highly reliant and robust model to solve this problem.
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In order to improve human judgement in diagnosis advent of new technology into health care can be witnessed. An entrance of computer vision into diagnosis would reduce human error in judgment. For example, examining MRI results for brain related ailments requires extremely high concentration and wrong judgement would be catastrophic. The MRI scans are capable of identifying even the smallest aberrations in the human body. Here we train a model to specifically identify these tiny aberrations from MRIs and predict presence of a tumor with high accuracy. Convolutional Neural Network (CNN) is one of the most effective techniques for this problem statement. Thus using image preprocessing and transfer learning using VGG16, we built a highly reliant and robust model to solve this problem.
Get Social With NC State ECE:
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