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GECC-Net: Gene Expression-Based Cancer Classification using Hybrid Fuzzy Ranking Network with Multi-kernel SVM

Gene selection is recognized as the basic procedure in the bioinformatics sector. The present approaches relevant to cancer categorization are primarily clinical foundation, and its diagnostic skill is restricted. Nowadays, the fundamental challenges of cancer detection are handled by the usage of gene expression data. The researchers have been providing several options to detect cancer accurately and efficiently. This paper aims to develop the gene expression-based cancer classification network (GECC-Net) using artificial intelligence methods. Initially, transfer learning based AlexNet is used to extract the features from the dataset by analyzing the relationship between various entities. Then, hybrid fuzzy ranking network (HFRN) is used to select the optimal feature selection fuzzy rules. Further, multi kernel support vector machine (MK-SVM) is used to perform the multi class classification operation, which performs the ovarian, colon, lymphography cancers classification. The simulation results shows that the proposed GECC-Net resulted in superior performance in comparison with state-of-art approaches.