Machine learning-based identification of radiofrequency electromagnetic radiation (RF-EMR) effect on brain morphology: a preliminary study.

The brain of a human and other organisms is affected by the electromagnetic field (EMF) radiations, emanating from the cell phones and mobile towers. Prolonged exposure to EMF radiations may cause neurological changes in the brain, which in turn may bring chemical as well as morphological changes in the brain. Conventionally, the identification of EMF radiation effect on the brain is performed using cellular-level analysis. In the present work, an automatic image processing-based approach is used where geometric features extracted from the segmented brain region has been analyzed for identifying the effect of EMF radiation on the morphology of a brain, using drosophila as a specimen. Genetic algorithm-based evolutionary feature selection algorithm has been used to select an optimal set of geometrical features, which, when fed to the machine learning classifiers, result in their optimal performance. The best classification accuracy has been obtained with the neural network with an optimally selected subset of geometrical features. A statistical test has also been performed to prove that the increase in the performance of classifier post-feature selection is statistically significant. This machine learning-based study indicates that there exists discrimination between the microscopic brain images of the EMF-exposed drosophila and non-exposed drosophila. Graphical abstract Proposed Methodology for identification of radiofrequency electromagnetic radiation (RF-EMR) effect on the morphology of brain of Drosophila.