An Optimal Deep Neural Network Model for Lymph Disease Identification And Classification

At present days, deep learning (DL) models find useful in various domains especially healthcare sector. This paper introduces a new DL based lymph disease identification and classification model. An optimal deep neural network (DNN) model is applied to classify the lymph data utilizing the stacked autoencoders (SA) which is generally used to extract the features from the dataset which is further classified by the utilization of SoftMax layer. In addition, to make the DNN model more efficient, DL based Adam optimizer (AO) is used which is an adaptive learning rate optimization algorithm which has been developed particularly to train DNN. The presented DNN with AO model called DNN-AO model is tested against benchmark Lymphography dataset and the results are assessed under diverse measures. The experimental outcome verified that effective classification performance is showcased by the DNN-AO model with the maximum sensitivity, specificity, accuracy and kappa value of 95.59, 95.95, 95.95 and 91.69 respectively.

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