This is a deep learning based model that can quantify patient’s risk of suffering from lung cancer and has ability to improve accuracy of cancer detection.The Lung CT images are preprocessed using slicing and sampling tecniques before feeding into 3D CNN. This model then identifies pattern related to cancerous nodules. Model can be used in assisting Radiologists and accurate diagnosis.
Input variables : DICOM Images
Output Variables : Quantify patient’s risk of suffering from lung cance
| 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 : medictiv.citiustech.com
| Model Category | : | Commercial |
| Date Published | : | September, 2018 |
| Healthcare Domain | : |
Medical Technology
Provider |
| Code | : | Not available |
| Medical Imaging |
| Health Risk Management |
| Disease Detection |