TAILIEUCHUNG - A hierarchical classifier for multiclass prostate histopathology image gleason grading

This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. | Journal of ICT, 17, No. 2 (April) 2018, pp: 323–346 How to cite this paper: Albashish, D., Sahran, S., Abdullah, A., Alweshah, M., & A., Adam. (2018). A hierarchical classifier for multiclass prostate histopathology image gleason grading. Journal of Information and Communication Technology, 17 (2), 323-346. A HIERARCHICAL CLASSIFIER FOR MULTICLASS PROSTATE HISTOPATHOLOGY IMAGE GLEASON GRADING Dheeb Albashish, 2Shahnorbanun Sahran, 2Azizi Abdullah, 3 Mohammed Alweshah & 2Afzan Adam 1&3 Prince Abdullah Ben Ghazi Faculty of Information Technology Al-Balqa Applied University, 19117 Al-Salt, Jordan 1&2 Faculty of Information Science and Technology Universiti Kebangsaan Malaysia, Selangor, Malaysia 1 bashish@;shahnorbanun@; azizia@; afzan@; weshah@ ABSTRACT Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and oneversus-all (Ovall). In these approaches, the multiclass problem is decomposed into numerous binary subtasks, which are separately addressed. However, OVALL introduces an artificial class imbalance, which degrades the classification performance, while in the case of OVO, the correlation between different classes not regarded as a multiclass problem is broken into multiple independent binary problems. This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. The proposed approach relies on the ‘divideReceived: 2 September 2017 Accepted: 4 January 2018 323 Journal of ICT, 17, No. 2 (April) 2018, pp: 323–346 and-conquer’ .

• Vật lý
• Hierarchical classifier for multiclass prostate histopathology image gleason grading
• Multiclass prostate histopathology image gleason grading
• New multiclass approach called multi level
• Several classes and the domain knowledge