Liver Tumour Segmentation Using Deep Learning

Baby, Albin (2024) Liver Tumour Segmentation Using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study focuses on improving the liver tumour segmentation by using state-of-art deep learning architectures including U-Net and V-Net integrated with residual networks such as ResNet and Inception. The study addresses the challenge of in methods used in medical image analysis particularly the detection of tumours is computationally expensive and is inaccurate. Transfer learning and a hybrid of loss functions (focal loss, Dice loss) increase computational efficiency to improve segmentation time from minutes to milliseconds, the results of empirical testing indicate an improvement of accuracy and the decrease of computational time of the model, based on such criteria as precision and recall, as well as the F1-score. These attributes have important clinical advantages as they accelerate and improve the accuracy of the diagnosis and location of liver tumours.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Uncontrolled Keywords:	Liver tumour segmentation; Medical Imaging; TensorFlow; Deep Learning; CNN
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Q Science > Life sciences > Medical sciences > Pathology > Tumors
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	01 Sep 2025 14:44
Last Modified:	01 Sep 2025 14:44
URI:	https://norma.ncirl.ie/id/eprint/8678

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