© The Institution of Engineering and Technology
Lung cancer is an insidious disease, producing no symptoms until the disease spreads widely in the human body. Mutations of genes are the first alarm of such a disease in the human body. Therefore, classifying these mutations could provide guidance for the treatment decisions for lung cancer. In this Letter, a novel accumulated grey-level image (AGLI) method for gene representation is introduced, where each base in gene sequence is represented by accumulated number based on its order in gene sequence and then reflected into image domain. AGLI is incorporated with 2D principle component analysis to build accurate and low-dimensional algorithm for classifying the genetic mutations. Proposed algorithm was applied on the top 10 effective genes in lung cancer, where an accuracy of 99.27% was achieved. Experimental results show that the proposed algorithm enhanced the accuracy of classification and reduced the classification time for mutation in lung cancer relative to the existing methods.
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