Abstract
Hyperspectral imaging approaches have proven its effectiveness in the medical field for characterizing brain tissues. Furthermore, these techniques in conjunction with machine learning (ML) algorithms has shown to be a useful tool for tumor detection in order to assist neurosurgeons in operations. In this report, it is proposed a novel deep recurrent neural network (DRNN) performing spectral recurrence with the hyperspectral bands to increase precision in tissue classification. In addition, this research present a comparison between the optimized models of the followings ML algorithms: support vector machine, random forest (RF) and the DRNN. All of them were trained by following an hyperparameter optimization process. As a result, DRNN improve brain tissue predictions in terms of the area under the receiver operating characteristic objective test metric (by 1.39% over SVM and 1.91% over RF) whereas RF classification maps illustrate truthfully the distribution of different tissue regions.
This work was supported by the Spanish Government through TALENT-HIPSTER project (PID2020-116417RB-C41).
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Cebrián, P.L. et al. (2023). Deep Recurrent Neural Network Performing Spectral Recurrence on Hyperspectral Images for Brain Tissue Classification. In: Chavarrías, M., Rodríguez, A. (eds) Design and Architecture for Signal and Image Processing. DASIP 2023. Lecture Notes in Computer Science, vol 13879. Springer, Cham. https://doi.org/10.1007/978-3-031-29970-4_2
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