Fractional-atom search algorithm-based deep recurrent neural network for cancer classification

Abstract

Deep learning has been paid great attention in several fields for the good reason, and their results achieved were not before possible. In deep learning, the models are trained based on huge labelled dataset and the neural network architectures consist of several layers. Still, the methods suffer to achieve expected higher performance and introduce poor classification accuracy. Towards improving the classification performance, this paper introduces the deep learning networks for the cancer classification application. The main aim is to develop the optimized model for classification application. Accordingly, the input data is pre-processed using Log transformation for converting the data to its uniform value range. Then, the feature selection is done based on Wrapper approach to select the important features for classifying the cancer. Once the features are selected, the cancer classification is performed using Deep Recurrent Neural Network (Deep RNN), which is trained by the proposed Fractional-Atom Search Algorithm (Fractional-ASO). The Fractional-ASO is designed by integrating Fractional Calculus with Atom Search Optimization (ASO). The performance of the Fractional-ASO-based Deep RNN is evaluated in terms of accuracy, True Positive Rate (TPR) and True Negative Rate (TNR). The proposed Fractional-ASO -based Deep RNN method achieves the maximal accuracy of 92.87%, maximal TPR of 92.87%, and the maximal TNR of 93.48% using colon dataset.