Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification for Lung Cancer Prediction 
F. Leena Vinmalar1, A. Kumar Kombaiya2
1F. Leena Vinmalar*, Research Scholar, Department of Computer Science, Chikkanna Government Arts College, Tirupur (Tamil Nadu), India.
2Dr. A. Kumar Kombaiya, Assistant Professor, Department of Computer Science Chikkanna Government Arts College, Tirupur (Tamil Nadu), India.
Manuscript received on April 20, 2021. | Revised Manuscript received on December 27, 2021. | Manuscript published on December 30, 2021. | PP: 90-95 | Volume-11 Issue-2, December 2021. | Retrieval Number: 100.1/ijeat.D25200410421 | DOI: 10.35940/ijeat.D2520.1211221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One of the major causes of cancer-related mortality worldwide is lung tumors. An earlier prediction of lung tumors is crucial since it may severely increase the death rates. For this reason, genomic profiles have been considered in many advanced microarray technology schemes. Amongst, an Improved Dragonfly optimization Algorithm (IDA) with Boosted Weighted Optimized Neural Network Ensemble Classification (BWONNEC) has been developed which extracts most suitable features and fine-tunes the weights related to the ensemble neural network classifiers. But, its major limitations are the number of learning factors in neural network and computational difficulty. Therefore in this article, a Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification (BWOCNNEC) algorithm is proposed to lessen the number of learning factors and computation cost of neural network. In this algorithm, the boosting weights are combined into the CNN depending on the least square fitness value. Then, the novel weight values are assigned to the features extracted by the IDA. Moreover, these weight values and the chosen features are processed in different CNN structures within the boosted classifier. Further, the best CNN structure in each iteration i.e., CNNs having the least weighted loss is selected and ensemble to predict and diagnose the lung tumors effectively. Finally, the investigational outcomes exhibit that the IDA-BWOCNNEC achieves better prediction efficiency than the existing algorithms.
Keywords: Lung tumor prediction, IDA, BWONNEC, Deep learning, Boosted CNN, Loss function.
Scope of the Article: Classification