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Detection of hydrocephalus using deep convolutional neural network in medical science

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Abstract

Hydrocephalus is a generally known disease found in the central nervous system and requires neurosurgical treatment. However, there is no prevalent solution and effective method for precise detection. This paper introduces Hydrocephalus detection based on the deep learning model which undergoes the stages like pre-processing, segmentation, feature extraction, and classification. Colour based transformation technique is used for better processing of input tested images. Then, these pre-processed images are segmented by mean shift clustering which is used to segment the image and to provide a reliable and accurate estimated value. Then the features are extracted using Complete Local Binary Pattern (CLBP). Finally, the classification uses Deep Convolutional Neural Network with Emperor Penguin Optimization (DCNN-EPO) for improving the system efficiency. The implementation of the developed scheme is implemented in PYTHON 3.7. At last, the performance of the developed scheme and the existing techniques are compared. The developed model achieves an accuracy of about 99.1%, sensitivity of about 98.5% and precision value of about 98.2% respectively. In addition, the average training and validation accuracy of the system is found to be 84.75% and 87.25% and the overall classification time of the developed model is 20.67 s only. Thus the proposed model proves its superiority against other models.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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  1. Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Chandanwadi, Sudhowala, Uttarakhand, 248007, India

    Dev Baloni

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

    Shashi Kant Verma

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  1. Dev Baloni
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Baloni, D., Verma, S.K. Detection of hydrocephalus using deep convolutional neural network in medical science. Multimed Tools Appl 81, 16171–16193 (2022). https://doi.org/10.1007/s11042-022-11953-w

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