Classification-Segmentation Pipeline for MRI via Transfer Learning and Residual Networks

• Autorzy: Duong-Trung Nghia , Ha Dung Ngoc Le , Huynh Hiep Xuan

Identyfikatory

DOI

10.15439/2021R14

• Języki publikacji: EN

Abstrakty

EN

Artificial intelligence association into brain magnetic resonance imaging (MRI) and clinical practices embracesubstantial cancer diagnosis improvement. The advancement ofdeep learning has improved the processing and analysis of MRI,boosting models' performance, decreasing the destructive effectsof data sources overload, and increasing accurate detection andtime efficacy. However, that specific dataset leads to diverseresearch fields such as image processing and analysis, detection, registration, segmentation, and classification. This paperproposes a decision-making pipeline for MRI data by combiningimage classification and segmentation. First, the pipeline shouldcorrectly produce a correct decision given an MRI image. If thefigure is classified as defective, the pipeline can extract defectregions and highlight them accordingly. We have implementedseveral advanced convolutional neural networks with transferlearning and residual techniques to address two broad clinicalconcerns in one decision-making workflow.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 27
• Strony: 39--43
• Opis fizyczny: Bibliogr. 27 poz., fot., tab., wykr.

Twórcy

autor

Duong-Trung Nghia

• Technische Universität Berlin Straße des 17. Juni 135, 10623 Berlin, Germany

autor

Ha Dung Ngoc Le

• Can Tho University of Technology 74000 Can Tho city, Vietnam

autor

Huynh Hiep Xuan

• Can Tho University 74000 Can Tho city, Vietnam

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).