Moment invariants for cancer classification based on electron–ion interaction pseudo potentials (EIIP)

Abstract

Cancer is considered one of the most dangerous diseases leading to imminent death during recent decades. It spreads quickly at a frightening pace to all parts of the body and early detection of such diseases may help reduce the risks. Many methods have been used to classify and predict the outcome of cancer, such as image processing techniques, artificial intelligence, and nanotechnology. Out of these methods, artificial intelligence techniques have played an essential role and have provided satisfying results from different investigations in various fields. Considering that cancer is a genetic disease, electron–ion interaction pseudo potential can be used to convert DNA sequences from string shape into number values so that genomic signal processing can be applied for the feature extraction step. In this paper, 51 healthy and 51 cancer genes from multiple cells obtained from the National Center for Biotechnology Information Genbank were used for analysis. Then, 7 invariant moments were extracted into 2 types of learning machine methods: supervised and unsupervised learning. Finally, their results were compared using Receiver Operating Characteristic parameters. From these results, the best accuracy was obtained from the trainable cascade-forward backpropagation network. The calculated parameters were 0.3333, 0.3158, 0.6842, 0.6667, and 67.5% for the Kohonen network, 0.1053, 0.1429, 0.8571, 0.8947, and 87.5% for the feed-forward network, and 0.0000, 0.0909, 0.9091, 1.0000, and 95% for the cascade-forward network for FPR, FNR, TPR, TNR, and accuracy respectively.