A related convolutional neural network for cancer diagnosis using microRNA data classification

This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs. The method's performance is boosted by the relationship between CNNs and exchanging knowledge between them. Besides, communication between small sizes of CNNs reduces the need for large size CNNs and, consequently, the computational time and memory usage while preserving high accuracy. The method proposed is tested on microRNA dataset containing the genomic information of 8129 patients for 29 different types of cancer with 1046 gene expression. The classification accuracy of the selected genes obtained by the proposed approach is compared with the accuracy of 22 well-known classifiers on a real-world dataset. The classification accuracy of each cancer type is also ranked with the results of 77 classifiers reported in previous works. The proposed approach shows accuracy of 100% in 24 out of 29 classes and in seven cases out of 29, the method achieved 100% accuracy that no classifier in other studies has reached. Performance analysis is performed using performance metrics.