@inproceedings{d5b4c4a10c164144873f97ad1cc4b3e1,
title = "Design and implementation of an optimized mask RCNN model for liver tumour prediction and segmentation",
abstract = "Segmentation of liver tumour is a tedious job due to their large variation in location and closeness to nearby organs. In this research, a novel Mask RCNN prototype is developed which uses ResNet-50 model. The architecture utilizes the masked location of convolution neural network to precisely detect liver tumours by recognizing liver sites to deal with changes in liver and CT snaps with distinct metrics. The preprocessed CT scans are subjected to ResNet-50 model. The data samples used here comprises 130 instances recorded from several clinical sites that are publicly available on the LiTS weblink. The designed model upon deployment generates a promising outcome thereby obtaining a DSC of 0.97%. Thus, we can conclude that the developed model is capable enough to accurately assess liver tumours and thus help patients in early diagnosis.",
keywords = "Liver Tumour segmentatio, Machine learning, CT- Image, convolution neural network, Mask RCNN, Liver Tumour segmentation",
author = "Raman Thakur and Volety, {Dayal Rohan} and Vandana Sharma and Sushruta Mishra and Celestine Iwendi and Jude Osamor",
year = "2024",
month = mar,
day = "5",
doi = "10.1109/ICCAKM58659.2023.10449653",
language = "English",
isbn = "9798350393255",
series = "2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023",
publisher = "IEEE",
booktitle = "Proceedings of ICCAKM 2023: 4th International Conference on Computation, Automation and Knowledge Management",
address = "United States",
}