IJCATR Volume 9 Issue 12

Constructive Learning of Deep Neural Networks for Bigdata Analysis

Soha Abd El-Moamen Mohamed , Marghany Hassan Mohamed , Mohammed F. Farghally
10.7753/IJCATR0912.1001
keywords : Health care, Deep learning, Constructive Deep learning, Diagnosis systems, Big data analysis

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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.
@artical{s9122020ijcatr09121001,
Title = "Constructive Learning of Deep Neural Networks for Bigdata Analysis",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "9",
Issue ="12",
Pages ="311 - 322",
Year = "2020",
Authors ="Soha Abd El-Moamen Mohamed , Marghany Hassan Mohamed , Mohammed F. Farghally "}