Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS.
| Autor: | Naga Srinivasu P; Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India., Balas VE; Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania. |
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| Jazyk: | angličtina |
| Zdroj: | PeerJ. Computer science [PeerJ Comput Sci] 2021 Aug 02; Vol. 7, pp. e654. Date of Electronic Publication: 2021 Aug 02 (Print Publication: 2021). |
| DOI: | 10.7717/peerj-cs.654 |
| Abstrakt: | In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach's performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training. Competing Interests: Valentina E. Balas is an Academic Editor for PeerJ. (©2021 Naga Srinivasu and Balas.) |
| Databáze: | MEDLINE |
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