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, Jude Osamor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of ICCAKM 2023: 4th International Conference on Computation, Automation and Knowledge Management
PublisherIEEE
Number of pages6
ISBN (Electronic)9798350393248
ISBN (Print)9798350393255
DOIs
Publication statusPublished - 5 Mar 2024

Publication series

Name2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
ISSN (Print)None

Keywords

  • Liver Tumour segmentatio
  • Machine learning
  • CT- Image
  • convolution neural network
  • Mask RCNN
  • Liver Tumour segmentation

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Control and Optimization
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Computer Science Applications

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