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Intelligent ICH Detection Using K-Nearest Neighbourhood, Support Vector Machine, and a PCA Enhanced Convolutional Neural Network

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Advances in Electrical and Computer Technologies (ICAECT 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 881))

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Abstract

Stroke is a critical condition with excessive mortality rate. The risk is largely from intracranial haemorrhage, and the primary causes are elevated blood pressure and trauma. Identification of haemorrhage is time critical, and it affects clinical management. Non-contrast computed tomography scans are pragmatic in disease confirmation and require the efforts of an expert radiologist. The impact of COVID-19 creates an extra burden on stroke care. We propose to develop an intelligent intracranial haemorrhage detection algorithm using K-nearest neighbourhood and support vector machine. The algorithm reported an accuracy of 85 and 87.5%. Further, we implemented a principal component analysis enhanced convolutional neural network (PCA-CNN) model that classified haemorrhage and normal subjects. The models achieved a sensitivity, specificity, and F1-score of 1.0, 0.91, and 0.95, respectively, for CNN and 1.0 each for PCA-CNN. We believe that our model can assist the radiologist in the clinical diagnosis of intracranial haemorrhage.

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Authors and Affiliations

  1. Department of ECE, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    Shanu Nizarudeen & Ganesh R. Shunmugavel

Authors
  1. Shanu Nizarudeen
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  2. Ganesh R. Shunmugavel
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Correspondence to Shanu Nizarudeen .

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Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, SVS College of Engineering, Coimbatore, India

    Thangaprakash Sengodan

  2. Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Safat, Kuwait

    M. Murugappan

  3. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

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Nizarudeen, S., Shunmugavel, G.R. (2022). Intelligent ICH Detection Using K-Nearest Neighbourhood, Support Vector Machine, and a PCA Enhanced Convolutional Neural Network. In: Sengodan, T., Murugappan, M., Misra, S. (eds) Advances in Electrical and Computer Technologies. ICAECT 2021. Lecture Notes in Electrical Engineering, vol 881. Springer, Singapore. https://doi.org/10.1007/978-981-19-1111-8_43

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  • DOI: https://doi.org/10.1007/978-981-19-1111-8_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1110-1

  • Online ISBN: 978-981-19-1111-8

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