Constructive Learning of Deep Neural Networks for Bigdata Analysis

Abstract

The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.

Dynamic multilayer growth: Parallel vs. sequential approaches

“…Replacing the pre-designed architecture of the fully-connected layer with a constructive algorithm gives rise to a more adaptive CNN. Mohamed et al [ 69 ] replaced the fully-connected layer in a CNN with their cascade-correlation growing deep learning neural network algorithm (CCG-DLNN) to successfully classify lung cancer images. With the success of a sequential growing approach in a CNN, the question arises could a parallel growing approach be beneficial to reduce time spent growing?…”