# Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radomic Features
## 4 Discussion  
Machine learning uses statistical analyses to combine various features for automatic classification. As a state-of-the art technique, deep learning inherited the methodology and exhausts computational power to automatically extract any possible features from a large dataset. Deep learning architectures such as the DCNN have been applied to a variety of classification tasks, including object detection and classification in natural images and medical images. DCNN architectures use multiple nonlinear transformations to stimulate advanced visual abstractions of image data. Inspired by the mechanism of biological nervous systems, multiple convolutional layers form a construction from pixels and transfer regions to objects to thoroughly analyze an image's composition. Based on the tissue appearance on a brain MR image, the proposed CAD system uses a transferred DCNN to establish a malignancy evaluation model to provide more objective and accurate diagnostic suggestions for grading gliomas. In this study, using the DCNN to classify grade 2, 3, and 4 gliomas achieved an almost perfect performance with a mean accuracy of 97.9% with an SD of \(\pm\)1% and a mean Az of 0.9991 \(\pm\) 0. A previous study [38] used well-known handcrafted MRI features, including the intensity and textural features, and achieved an accuracy of only 88%, a sensitivity of 82%, a specificity of 90%, and an Az of 0.89. Regarding the classifier type, the performance of conventional artificial neural networks in a previous study [38]  
Figure 5: Training and validation learning processes. Figure 6: Test accuracy illustrated by tradeoffs between the sensitivity and specificity. had a lower diagnostic performance in comparison: an accuracy of 84%, a sensitivity of 79%, and a specificity of 86%. In a study by [PERSON] [29], they used 113 gliomas and convolutional neural networks with and without transfer learning. The results demonstrated that using transfer learning can improve the performance, which is similar to our results. [PERSON] used automatic segmentation methods to obtain the tumor contours and areas, just like in our delineation. The following classification also depends on machine learning techniques [30]. Another study used diagnostic information from multiple modalities to achieve better performance [39]. [PERSON] et al. [31] imposed a transfer learning model to classify optical coherence tomographic images and achieved high performances of accuracy, sensitivity, and specificity exceeding 93% and an Az of 0.988. It is recommended that training using medical images with pretrained models may produce more accurate models in a very short time compared to training a model from scratch. Even with a limited number of datasets, the transferred DCNN can generate comparable or even better performances than human experts. Similar results were shown in this study, as the transferred DCNN performed much better than the DCNN without pretrained features and the DCNN without augmentation. The difference was likely caused by the number of training samples. The original dataset without augmentation contained only 30, 43, and 57 images in the three classes, respectively which is very low even when using transfer learning--compared to [PERSON] et al., who used 1000 images in each class to train a limited model. Through AutoAugment, described in this study, the dataset was expanded 57-fold and therefore contained over 1000 images in each class. The total number of augmented datasets was sufficient for transfer learning, but still not for original deep learning, so the result of the DCNN without augmentation was worse than that of the transferred DCNN. Based on the success of transfer learning, collecting qualified image data, such as precise labeling and description of the tumor location, boundaries, and features, may become more important than thinking about sophisticated image features. However, collecting substantial qualified medical data is very challenging.