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Detection of Liver Cirrhosis using a Web-Based Convolutional Neural Network System

Author

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  • Adeoti Babajide E
  • Lawani Benjamin
  • Ayoola Oluwatorera
  • Mgbeahuruike Emmanuel
  • Adebanjo Samuel A
  • Oladunjoye Michael

Abstract

Liver cirrhosis, a critical health condition marked by irreversible scarring of the liver, contributes significantly to global morbidity and mortality. Traditional diagnostic methods are invasive, costly, and often detect the disease at advanced stages. This study presents the design and implementation of a non-invasive, web-based liver cirrhosis detection system employing Convolutional Neural Networks (CNNs). The system aims to support early diagnosis and prognosis using medical imaging and machine learning. The implemented system demonstrates high accuracy in classifying cirrhotic conditions and offers a scalable solution for under-resourced medical facilities

Suggested Citation

  • Adeoti Babajide E & Lawani Benjamin & Ayoola Oluwatorera & Mgbeahuruike Emmanuel & Adebanjo Samuel A & Oladunjoye Michael, 2025. "Detection of Liver Cirrhosis using a Web-Based Convolutional Neural Network System," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(4), pages 316-321, August.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i4:id:1635
    DOI: 10.32628/CSEIT25113395
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25113395
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