Core i7 processor

Data from a simulation study and in vivo data of healthy volunteers as well as patients with different brain lesions were used. The processing and visualization was performed on a Linux (Ubuntu 14.04) workstation with an Intel Core i7 processor, 8 GB RAM, and NVIDIA GeForce GT425 graphics card.

Two different approaches (1) feature extraction-based (DL models: AlexNet, GoogLeNet, Inception V3, MobileNet V2, and ResNet50) and (2) function approximation-based ML models (Artificial neural network (ANN), K-nearest neighbors (KNN), Support vector machine (SVM), and Logistic regression (LR)); and a DL model (DL-LSTM) were adopted for crop water stress classification. Feature extraction-based models were trained on thermal as well as RGB imagery. Function approximation-based models were trained on ambient weather and soil parameters, and T_c inputs from thermal imagery.
Deep CNNs typically have complex architecture and some may require significant computational resources. All CNN model training and validation processes were performed on a desktop computer (Intel Core I7 Processor with base frequency 2.60 GHz, 16 GB RAM, 6 GB NVIDIA GeForce GTX 1660 Ti GPU) with Windows 10 operating system (64 bits). CNN models were developed in MATLAB 2019b using the deep learning and machine learning toolbox. All the models are detailed in the following sub-sections.

One of the potential implications of obtaining recurrence probability maps of the peritumor region is to adapt the surgical strategy by extending the resection to these areas. The resulting best-trained model was applied to the preoperative MRI scans of the test cohort following the same preprocessing steps mentioned above. The results were qualitatively reviewed by an experienced neurosurgeon (SC).
Once the classifier was trained, the average time required for processing a new patient, including image preprocessing, segmentation, extraction of radiomic features, and model application, was approximately 45 min. A computer with a 2.20 GHz Intel Core i7 processor, 32 GB of RAM, and a 16 GB NVIDIA GeForce RTX 3070 graphics card was used.