Deep Learning and Transfer Learning for Brain Tumor Detection and Classification
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.10.536226v1?rss=1

Authors: Rustom, F., Parva, P., Ogmen, H., Yazdanbakhsh, A.

Abstract: Convolutional neural networks (CNNs) are powerful tools that can be trained on image classification tasks and share many structural and functional similarities with biological visual systems and mechanisms of learning. In addition to serving as a model of biological systems, CNNs possess the convenient feature of transfer learning where a network trained on one task may be repurposed for training on another, potentially unrelated, task. In this retrospective study of public domain MRI data, we investigate the ability of neural networks to be trained on brain cancer imaging data while introducing a unique camouflage animal detection transfer learning step as a means of enhancing the network tumor detection ability. Training on glioma, meningioma, and healthy brain MRI data, both T1- and T2-weighted, we demonstrate the potential success of this training strategy for improving neural network classification accuracy both quantitatively with accuracy metrics and qualitatively with feature space analysis of the internal states of trained networks. In addition to animal transfer learning, similar improvements were noted as a result of transfer learning between MRI sequences, specifically from T1 to T2 data. Image sensitivity functions further this investigation by allowing us to visualize the most salient image regions from a network perspective while learning. Such methods demonstrate that the networks not only look at the tumor itself when deciding, but also at the impact on the surrounding tissue in terms of compressions and midline shifts. These results suggest an approach to brain tumor MRIs that is comparatively similar to that of trained radiologists while also exhibiting a high sensitivity to subtle structural changes resulting from the presence of a tumor.

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