Abstract
Brain tumors are the most common undermining malignancies in people of all ages. The most challenging difficulty for medical specialists and radiologists in the detection of automated brain illnesses and issues is grade recognition. Various deep learning-based techniques have recently been presented for identifying brain tumors in order to enhance the analytic examination. The proposed research work outlines an in-depth assessment of the research that has already been published, as well as current approaches based on deep learning for brain tumor categorization. The proposed analysis of the literature covers the essential phases of deep learning-based methods for brain tumor classification. This includes pre-processing, highlighted feature extraction and classification alongside their benefits and limits. Finally, this review not just investigates the previous surveys on the point but also enumerates various research proposals that ought to be continued in the future, particularly for customized and smart health monitoring framework.
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Sheethal, M.S., Amudha, P., Sivakumari, S. (2022). An Intelligent Survey on Deep Learning-Based Strategies for Multi-Grade Brain Tumor Classification. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_60
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