Open AccessBrain Tumour Image Classification Using Learning Vector Quantization Based Zoning Method
Author(s) -
Fahmi Fahmi,
Fitria Priyulida,
Suherman Suherman
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1235/1/012027
Subject(s) - learning vector quantization , artificial intelligence , vector quantization , zoning , pattern recognition (psychology) , computer science , artificial neural network , identification (biology) , quantization (signal processing) , computer vision , engineering , biology , civil engineering , botany
Brain tumour identification has been increasingly important area, mainly by using CT scan. Neural network and artificial intelligence methods dominate the processing algorithms; however, new methods are expected to emerge. This paper discusses brain tumour image classification by zoning combination using learning vector quantization (LVQ). The matrix results of the zoning are used as the LVQ inputs. As results from the assessment of the twenty normal and abnormal brain images, identification has been successfully carried out by 80% and 90% subsequently for abnormal and normal brain.