A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning

Abstract

A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning Singh A, Singh N, Jinda T, Rosado-Muñoz, A, Kishore Dutta M. A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning. Biomedical Signal Processing and Control. Vol. 57, March 2020, 101821. doi.org. Highlights • This work presents automated identification of the effect of EMF radiations on brain. • Changes in brain morphology due to EMF exposure were analyzed considering drosophila melanogaster as a specimen. • The geometrical features were extracted from the microscopic segmented brain image of drosophila. • Machine learning techniques were used for identification of EMF exposure on drosophila brain. Abstract Electromagnetic field (EMF) radiations from mobile phones and cell tower affect brain of humans and other organisms in many ways. Exposure to EMF could lead to neurological changes causing morphological or chemical changes in the brain and other internal organs. Cellular level analysis to measure and identify the effect of mobile radiations is an expensive and long process as it requires preparing the cell suspension for the analysis. This paper presents a novel pilot study to identify changes in brain morphology under EMF exposure considering drosophila melanogaster as a specimen. The brain is automatically segmented, obtaining microscopic images from which discriminatory geometrical features are extracted to identify the effect of EMF exposure. The geometrical features of the microscopic segmented brain image of drosophila are analyzed and found to have discriminatory properties suitable for machine learning. The most prominent discriminatory features were fed to four different classifiers: support vector machine, naïve bayes, artificial neural network and random forest for classification of exposed / non-exposed microscopic image of drosophila brain. Experimental results indicate that all four classifiers provide good classification results up to 94.66% using discriminatory features selected by feature selection method. The proposed method is a novel approach to identify the effect of EMF exposure automatically and with low time complexity thus providing an efficient image processing framework based on machine learning. sciencedirect.com ence/article/abs/pii/S1746809419304021

AI evidence extraction

Main findings

Microscopic segmented brain images of Drosophila under EMF exposure showed geometrical features with discriminatory properties that enabled machine-learning classifiers to distinguish exposed vs non-exposed brains, with reported classification performance up to 94.66% using selected features.

Outcomes measured

• Brain morphology changes (microscopic image-derived geometrical features)
• Classification accuracy for exposed vs non-exposed brains using machine learning (SVM, naïve Bayes, ANN, random forest)

Limitations

• Pilot study (as described)
• No exposure parameters reported (e.g., frequency, SAR, duration)
• Sample size not reported
• Outcome is indirect (image-feature discrimination/classification) rather than direct biological/clinical endpoints

Suggested hubs

• animal-studies (0.86)
  Uses Drosophila melanogaster to assess brain morphology under EMF exposure.

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    "study_type": "animal",
    "exposure": {
        "band": null,
        "source": "mobile phone and cell tower",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": "drosophila melanogaster",
    "sample_size": null,
    "outcomes": [
        "Brain morphology changes (microscopic image-derived geometrical features)",
        "Classification accuracy for exposed vs non-exposed brains using machine learning (SVM, naïve Bayes, ANN, random forest)"
    ],
    "main_findings": "Microscopic segmented brain images of Drosophila under EMF exposure showed geometrical features with discriminatory properties that enabled machine-learning classifiers to distinguish exposed vs non-exposed brains, with reported classification performance up to 94.66% using selected features.",
    "effect_direction": "harm",
    "limitations": [
        "Pilot study (as described)",
        "No exposure parameters reported (e.g., frequency, SAR, duration)",
        "Sample size not reported",
        "Outcome is indirect (image-feature discrimination/classification) rather than direct biological/clinical endpoints"
    ],
    "evidence_strength": "very_low",
    "confidence": 0.66000000000000003108624468950438313186168670654296875,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "electromagnetic field",
        "EMF",
        "mobile phone",
        "cell tower",
        "drosophila melanogaster",
        "brain morphology",
        "microscopy",
        "image segmentation",
        "computer vision",
        "machine learning",
        "support vector machine",
        "naïve bayes",
        "artificial neural network",
        "random forest"
    ],
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}

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