Modified MobileNet for Patient Survival Prediction

Glioblastoma is a type of malignant tumor that varies significantly in size, shape, and location. The study of this type of tumor, one of which is about predicting the patient’s survival ability, is beneficial for the treatment of patients. However, the supporting data for the survival prediction model are minimal, so the best methods are needed for handling it. In this study, we propose an architecture for predicting patient survival using MobileNet combined with a linear survival prediction model (SPM). Several variations of MobileNet are tested to obtain the best results. Variations tested include modification of MobileNet V1 with freeze or unfreeze layers, and modification of MobileNet V2 with freeze or unfreeze layers connected to SPM. The dataset used for the trial came from BraTS 2020. A modification based on the MobileNet V2 architecture with the freezing layer was selected from the test results. The results of testing this proposed architecture with 95 training data and 23 validation data resulted in an MSE Loss of 78374.17. The online test results with the validation dataset 29 resulted in an MSE loss value of 149764.866 with an accuracy of 0.345. Testing with the testing dataset resulted in increased accuracy of 0.402. These results are promising for better architectural development.