Magnetic Resonance Imaging (MRI) is used by radiotherapists to manually segment brain lesions and to observe their development throughout the therapy.In this work, FCNs are constructed with pretrained convolutional encoders to identify Tumor from MRI scans.The manual image segmentation process is time-consuming and results tend to vary among different human raters but ML algorithms can be faster and show better accuracy.The method is tested on Clincal images as well as BraTS challenge dataset.
Input variables : MRI Scan
Output Variables : Tumor Segmentation
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : arxiv.org
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | November, 2020 |
| Healthcare Domain | : | Medical Technology |
| Code | : | github.com |
| Medical Imaging |
| Health Risk Management |
| Disease Detection |