ArticleDiagnostics (Basel, Switzerland)2022
Brain Stroke Classification via Machine Learning Algorithms Trained with a Linearized Scattering Operator.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Quantum SVM-driven framework for accurate brain stroke classification.Scientific reports · 2026Article
- A Multi-Input Neural Network for Microwave Hemorrhagic Stroke Identification Using Multimodal Data.Brain sciences · 2026Article
- Early Detection of Alzheimer's Disease via Machine Learning-Based Microwave Sensing: An Experimental Validation.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning in Microwave Medical Imaging and Lesion Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Near-field microwave sensing technology enhanced with machine learning for the non-destructive evaluation of packaged food and beverage products.Scientific reports · 2024Article
- On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification.Sensors (Basel, Switzerland) · 2023Article
- Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers.Frontiers in bioengineering and biotechnology · 2023Article
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Authors and funding
4 authors.
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Abstract
This paper proposes an efficient and fast method to create large datasets for machine learning algorithms applied to brain stroke classification via microwave imaging systems. The proposed method is based on the distorted Born approximation and linearization of the scattering operator, in order to minimize the time to generate the large datasets needed to train the machine learning algorithms. The method is then applied to a microwave imaging system, which consists of twenty-four antennas conformal to the upper part of the head, realized with a 3D anthropomorphic multi-tissue model. Each antenna acts as a transmitter and receiver, and the working frequency is 1 GHz. The data are elaborated with three machine learning algorithms: support vector machine, multilayer perceptron, and k-nearest neighbours, comparing their performance. All classifiers can identify the presence or absence of the stroke, the kind of stroke (haemorrhagic or ischemic), and its position within the brain. The trained algorithms were tested with datasets generated via full-wave simulations of the overall system, considering also slightly modified antennas and limiting the data acquisition to amplitude only. The obtained results are promising for a possible real-time brain stroke classification.
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Registered trials
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