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Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

Sindhuja Manickavasagam1,*, Poonkuzhali Sugumaran2

1 Department of Information Technology, Rajalakshmi Engineering College, Chennai, 600125, Tamilnadu, India
2 Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, 600125, Tamilnadu, India

* Corresponding Author: Sindhuja Manickavasagam. Email:

Computer Systems Science and Engineering 2023, 45(1), 939-953.


The combination of machine learning (ML) approaches in healthcare is a massive advantage designed at curing illness of millions of persons. Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis. Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world. Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs. The previous detection of cancer is not simpler procedure however if it can be detected, it can be curable, also finding the survival rate is a major challenging task. This study develops an Ant lion Optimization (ALO) with Deep Belief Network (DBN) for Lung Cancer Detection and Classification with survival rate prediction. The proposed model aims to identify and classify the presence of lung cancer. Initially, the proposed model undergoes min-max data normalization approach to preprocess the input data. Besides, the ALO algorithm gets executed to choose an optimal subset of features. In addition, the DBN model receives the chosen features and performs lung cancer classification. Finally, the optimizer is utilized for hyperparameter optimization of the DBN model. In order to report the enhanced performance of the proposed model, a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.


Cite This Article

S. Manickavasagam and P. Sugumaran, "Optimal deep belief network based lung cancer detection and survival rate prediction," Computer Systems Science and Engineering, vol. 45, no.1, pp. 939–953, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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