Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation
- Title
- Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation
- Creator
- Thakur R.; Volety D.R.; Sharma V.; Mishra S.; Iwendi C.; Osamor J.
- Description
- 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. 2023 IEEE.
- Source
- 2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- convolution neural network; CT- Image; Liver Tumour segmentation; Machine learning; Mask RCNN
- Coverage
- Thakur R., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Volety D.R., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Sharma V., Christ (Deemed to Be University), Computer Science Department, Delhi NCR, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, Glasgow, G4 OBA, United Kingdom
- Rights
- All Open Access; Green Open Access
- Relation
- ISBN: 979-835039324-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Thakur R.; Volety D.R.; Sharma V.; Mishra S.; Iwendi C.; Osamor J., “Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19642.