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Early Detection of Glioblastoma Multiforme Brain Cancer through the Implementation of Convolutional Neural Networks on MRI Imaging

Despite great advances in the field of oncology, glioblastoma multiforme’s extreme aggression still results in a grim prognosis. A median survival length of a mere 11-15 months, couples with one of the lowest survival rates of all cancers, at ~4%.

Simultaneously, detection models have made great progress with non-small cell lung cancer and Alzheimer's in the medical diagnostics industry, while the current conventional detection of brain tumors involves human inspection of radiological imagery for tissue abnormalities. Our project aims to utilize convolutional neural networks on MR imaging for the same purpose. Work was primarily split into three categories: data extraction and preprocessing of imagery, convolutional neural network training, and the machine learning classification period.

The automation of early-stage tumor detection drastically reduces the workload of radiologists, aids with patient outcomes through earlier treatment, and may provide insight into the characteristics of high-grade astrocytomas. MR Imaging and ML algorithms look promising regarding their potential applications in the medical field, particularly in the field of medical diagnoses.

Model-11 Accuracy and Results

Dataset Accuracy Loss Dataset size
Training 0.9897 0.0380 270179
Testing 0.9964 0.0149 30018

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