Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey

By Khan Muhammad, Salman Khan, Javier Del Ser, V. H. C. de Albuquerque. IEEE Transactions on Neural Networks and Learning Systems, 2020

325 citations recorded by the source corpus.

Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/8f9d5f33413613c3ffd32841846adaf358dc05d5

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, background): “[36] presented a detailed review on multi-grade brain tumor classification.”
Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, background): “In addition, because existing methods lack precision and recall, they are inefficient and take too long to classify images, which can postpone the start of treatment [28].”
IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare - A Review (2021, background): “8 Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey [14] N Y Y N”