Abstract
In the study, we used two neural networks, including VGG16 and Resnet50, to process the whole slide images with feature extracting. To classify the three types of brain tumors (i.e., glioblastoma, oligodendroglioma, and astrocytoma), we tried several clustering methods include k-means and random forest classification methods. In the prediction stage, we compared the prediction results with and without MRI features. The results support that the classification method performed with image features extracted by VGG16 has the highest prediction accuracy. Moreover, we found that combining with radiomics generated from MR images slightly improved the accuracy of the classification.
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Chan, HW., Weng, YT., Huang, TY. (2020). Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology Images. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_35
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