An Optimal Deep Wavelet Autoencoder-Based DNN with the use of Rider Cuckoo Search Algorithm for classification of the lung cancer on CT images

Abstract

The only way to enhance a patient's survival chances is to recognize the lung cancer early. A CT scan is utilized to determine the spot of a tumour and the extent of illness in the body. The CT scan of lung images was analysed in this study using an Optimal Deep Wavelet Autoencoder-Based DNN (ODWADNN). Using the Accelerated Greedy Snake's algorithm, a highly accurate, dependable, fast, automated paradigm was utilized to segment the liver tumour image (AGSA). In this case, the recommended RCSA is utilized to train the DBN. The recommended RCSA combines the ROAand the Cuckoo Search algorithm (CS). The discussed paradigm enhances the mentioned disease prediction rate, measured by MATLAB-based outcomes such as Reliability, Specificity, Precision, Recall, and F1 score.