Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification

Abstract

Lymphatic system reinforces immune system by degrading as well as eliminating the cancer cells, and pathogens, rejecting unwanted sources, debris, and dead blood cells. It assists in assimilating the fat vitamins and fat-soluble from digestive system and delivers them to body tissues. Furthermore, the interstitial spaces amongst cells eradicate the extra fluids and redundant substances from body. Automatic diagnosis of cancer metastases in lymph nodes has the prospective to increase calculation of prognoses for patients. Machine learning¬based classification methods offer provision for the decision¬making method in various regions of healthcare, involving screening, diagnosis, prognosis, and so on. This study introduces an Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification (OFFDNN-LDC) model. The presented OFFDNN-LDC model intends to apply the classification model to determine the presence of lymph diseases in medical data. For attaining this, the presented OFFDNN-LDC model exploits the FFDNN model as a classifier to assign effective class labels. Besides, the presented OFFDNN-LDC model executes root mean square propagation (RMSProp) optimizer to properly elect the hyperparameter values of the FFDNN model. A series of simulations are performed for demonstrating the improved outcome of the OFFDNN-LDC model. The experimental values referred that the OFFDNN-LDC model is superior to other models.