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Detection of hydrocephalus using machine learning in medical science – a review

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Abstract

Machine learning (ML) is the study of computer algorithms that expand spontaneously by knowledge. ML algorithms construct an analytical model centred on sample data, recognized as ‘training data,’ in order to make projections or conclusions without being specifically programmed to do so. Hydrocephalus is the generally known disease found in children of the central nervous system and requires neurosurgical treatment and that has been studied and imaged for years however, there is still no prevalent solution and effective method for precise detection and computable evaluation of this. This work suggests a modern form of Machine Learning (ML) for the early detection of hydrocephalus. ML is the fast growing and challenging field now days. For medical diagnosis, ML methods are used. Four phases are involved in the identification of hydrocephalus using image processing methods, namely image pre-processing, image segmentation, detection and classification of features.

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Authors and Affiliations

  1. Computer Science & Engineering, Uttarakhand Technical University, Sudhowala, India

    Dev Baloni

  2. Govind Ballabh Pant Institute of Engineering and Technology, Pauri, Garhwal, India

    Shashi Kant Verma

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  1. Dev Baloni
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  2. Shashi Kant Verma
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Correspondence to Dev Baloni.

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Baloni, D., Verma, S.K. Detection of hydrocephalus using machine learning in medical science – a review. Multimed Tools Appl 81, 21199–21222 (2022). https://doi.org/10.1007/s11042-022-12744-z

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  • DOI: https://doi.org/10.1007/s11042-022-12744-z

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