The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Identification of Hepatocellular Carcinoma From CT Liver Image for CAD Systems
|
|
Author(s): R. Prabakaran (MIT Campus, Anna University, India), D. Shiloah Elizabeth (CEG, Anna University, India)and C. Sunil Retmin Raj (MIT Campus, Anna University, India)
Copyright: 2025
Pages: 28
Source title:
Signal and Image Processing Techniques for Defense, Security, and Healthcare
Source Author(s)/Editor(s): B. Omkar Lakshmi Jagan (Vignan's Institute of Information Technology, India), Amrit Mukherjee (University of South Bohemia, Czech Republic), Thayyaba Khatoon Mohammed (Malla Reddy University, India)and Vustikayala Sivakumar Reddy (Malla Reddy University, India)
DOI: 10.4018/979-8-3693-3840-7.ch008
Purchase
|
Abstract
Liver cancer is the sixth most common cancer. Liver cancer is frequently analyzed with Computed Tomography (CT) scan. Early diagnosis by CT could lead to high recovery rate, however going through all the CT slices for thousands or even millions of patients manually by professionals is hard, expensive, inefficient and prone to errors. The proposed method mainly focuses on precision of liver lesion segmentation and to improve performance of the CAD systems. The goal is to create a reliable Res-UNet model that can segment the liver, identify Regions of Interest (RoI) from nearby organs, and use that information to identify liver lesions using a different Res-UNet. The contributions of this chapter are identifying whether liver is healthy or affected by the disease (hepatocellular carcinoma), segmenting lesions from the extracted liver image using Res-UNet and to evaluate the accuracy of segmentation. The developed models have been tested using LiTS dataset. The accuracy of liver segmentation and lesion segmentation achieved using ResUNet are 0.9918 and 0.99, respectively.
Related Content
|
R. N. Ravikumar, S. Aarthi, Valisher Sapayev, Alijon Esanov.
© 2026.
32 pages.
|
|
Md Mehedi Hasan Emon, Tahsina Khan.
© 2026.
34 pages.
|
|
Zerin Tasnim, Md Mahdi Hasan Ahid, Md. Adnan Rahman, Mohammad Mofasserul Islam, Md. Nafis Fuad, Abu Bakar Abdul Hamid.
© 2026.
34 pages.
|
|
P. S. Venkateswaran, S. Jeyakumar, S. Devi Kamatchi, S. Manimaran.
© 2026.
36 pages.
|
|
Aliza, Abdullah, Muhammad Usman.
© 2026.
32 pages.
|
|
Rohit Yadav.
© 2026.
22 pages.
|
|
Salam Al E'mari, Yousef Sanjalawe, Fuad Fataftah.
© 2026.
30 pages.
|
|
|