GeneNet: Transfer learning-based hybrid African buffalo optimization with genetic algorithm for gene expression based cancer classification

Abstract

Gene therapy is one of the advanced medical diagnosis environments, which is used to identify the solutions to various cancers by identifying the perfect gene expressions. The computer aided detection of gene expressions is a research problem, where cancer type is identified by artificial intelligence methods. However, the conventional machine learning models were failed to provide the accurate cancer classification and resulted in poor quantitative performance. Therefore, this work focuses on the implementation of gene expression-based cancer classification (GECC) network, here after referred as GeneNet. Initially, the proposed GeneNet performs the data preprocessing to normalize the gene-expression data by replacing the unknown characters and missing symbols. Then, the visual geometry group developed VGG16 model for extraction of correlated, and structural features from pre-processed dataset. In addition, hybrid African buffalo optimization with genetic algorithm (HABO-GA) is used to select the optimal features through natural inspired hunting (searching) properties. Finally, low complex convolutional neural network (LC-CNN) model is trained with HABO-GA features, which also performs the multi-class cancer classification from test data. The simulations are conducted on publicly available GECC datasets such as lymphography, ovarian, leukemia, lung, and prostate cancers discloses the superiority of proposed GeneNetas compared to state-of-the-art approaches with 86.6 %, 100 %, 76.92 %, 100 %, and 100 % of accuracy, respectively.