Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation

Raman Thakur, Dayal Rohan Volety, Vandana Sharma, Sushruta Mishra, Celestine Iwendi and Jude Osamor 2023. Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation. 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM). Dubai, United Arab Emirates 12 - 13 Dec 2023 IEEE . https://doi.org/10.1109/iccakm58659.2023.10449653

TitleDesign and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation
AuthorsRaman Thakur, Dayal Rohan Volety, Vandana Sharma, Sushruta Mishra, Celestine Iwendi and Jude Osamor
TypeConference paper
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.

Year2023
Conference2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM)
PublisherIEEE
Accepted author manuscript
File Access Level
Open (open metadata and files)
Publication dates
Published12 Dec 2023
Journal2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM)
ISBN9798350393248
Digital Object Identifier (DOI)https://doi.org/10.1109/iccakm58659.2023.10449653
Web address (URL)http://dx.doi.org/10.1109/iccakm58659.2023.10449653

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