Cervical Cancer Diagnosis Using Intelligent Living Behaviour of Artificial Jellyfish Optimized with Artificial Neural Network

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Devarajan, Devikanniga, Stalin Alex, D, Mahesh, T R, Aluvalu, Rajanikanth, Vinoth Kumar, V, Maheswari, Uma, Shitharth, S
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Jellyfish search optimizer; Artificial neural network; Artificial neural networks; Cancer; Cervical cancer; Classification algorithms; Diagnosis; Heuristic methods; Metaheuristic optimization; Mortality; Neural networks; Neurons; Random forests; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3221451

Cervical cancer affects nearly 4% of the women across the globe and leads to mortality if not treated in early stage. A few decades before, the mortality rate was too high when compared to the present statistics. This is achieved as nowadays most of women are aware of this disease and undergo health examination mainly for screening cervical cancer on regular basis. But only the accurate diagnosis can be helpful for further treatment. Many works are carried out for accurate diagnosis and always have some limitations in accurate predictions. In this work, an efficient algorithm is proposed for the accurate diagnosis of cervical cancer. A meta-heuristic called artificial Jellyfish search optimizer (JS) algorithm is combined with artificial neural network (ANN) to tackle this problem. The proposed algorithm is called JellyfishSearch_ANN and is employed to classify the cervical cancer dataset with four type of targets based on the examination. The JellyfishSearch_ANN provides outstanding results among other classifiers taken for comparison and mainly its classification accuracy is found to be above 98.87% for all targets.