Improving the Performance of Lung Cancer Detection at Earlier Stage and Prediction of Reoccurrence using the Neural Networks and Ant Lion Optimizer

Lung cancer is considered to be the one among the most dreaded disease which will be the main reason for the death of individuals and having greater deterioration of death if it is not identified at primitive stage. Because of the fact that Lung cancer could be identified only after spreading to the parts of lungs to a greater extent and it is very tough to predict the presence of lung cancer at the earlier stage. Moreover, it involves greater error in the diagnosing the presence of Lung cancer by Radiologists and Expert Doctors. Therefor it is compulsory to design an intelligent and automated system for accurately predicting the cancer and stage at which the stage of cancer or enhancing the accuracy of prediction for detecting the cancer at earlier which will be much helpful in deciding the treatment type and depth of the treatment based on the extent of disease. Currently application of ANN strategies are the influential ways in supporting expert doctor for examining, complicated medical increase across a wider category of medical application. Back Propagation Network are ideal in recognizing lung cancer and there is no requirement involvement by expert doctors. Maximum number of applications of BPN in medical diagnosis will be utilized in the applications related to decision making of the presence or absence of disease; by which the performance will be reliant over the considered features and allocating the patient with minimum number of classes. Here this research paper establishes the idea of using BPN in the classification of the lung cancer and its stages and the predicting the possibility of recurrence. Along with the BPN, a nature inspired Meta Heuristics that is termed as Ant Lion Optimization Algorithm is used in optimizing the parameters and weights of Back Propagation Network. By using the Ant Lion Optimization Algorithm, the convergence mechanism is improved along with improving the accuracy of the proposed technique and it avoids the chance of getting caught within the clutches of local minima. By using this proposed method BPN network optimized with the help of antlion optimizer more accurate prediction of lung cancer is possible even at primitive stage and the predicting of chance of reoccurrence even after undergoing the appropriate treatment.

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Machine Learning Is the Future for Lung Cancer Prognosis and Prediction

Applications of Deep Learning and Big IoT on Personalized Healthcare Services - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-7998-2101-4.ch011 ◽

2020 ◽

pp. 176-196

Author(s):

Ayshwarya Balakumar ◽

Senthil S.

Keyword(s):

Lung Cancer ◽

Back Propagation ◽

Hiv Infections ◽

Cancer Prognosis ◽

Ant Lion Optimization ◽

Prognosis And Prediction ◽

Characteristic Features ◽

The One ◽

Propagation Network ◽

Lion Optimization Algorithm

Lung cancer is one of the major reasons for the death if it is not diagnosed in the early stages of cancer. It is the one among the most dreadful disease which affects in the lungs function. It can be identified only after the disease spread into the deeper parts of the lungs and then only it will make a life threading problem. Lung cancer prognosis which was done based on the various parameters such as age, sex, condition of smoking, duration of smoking and count of smoking per day. The proceedings were done using the algorithm for the time to first cigarette after awakening which is represented as TTFC. The expert doctor says that the back-propagation network is a great deal in the recognition of the lung cancer without any involvement by them. This research is based on the classification of lung cancer and its stages using the establishment of the BPN and predicts the recurrence. Similarly, with this BPN, an algorithm that is inspired from its habitat known as ant lion optimization algorithm is also used in the optimization of weights and parameters of the BPN. The use of the ALO algorithm provides an improved convergence mechanism by improving the proposed technique's accuracy. The use of this proposed method with the BPN optimizes the network and the ALO optimizer provides an accurate prediction of the lung cancer by the earlier stage and even predicts the changes for reoccurrence after diagnosis. The prognosis analysis was made by the various comparative study between the characteristic features of HIV and the unaffected person using the algorithm such as the Wilcoxon rank-sum test. This algorithm will continuously classify the viral load and CD4 count which is based on factors such as age, sex, and smoking activities. It will be useful for early diagnosis and future prediction. Lung cancer rates can be analyzed based on the incident rates of affected and unaffected persons to HIV infections.

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Development of an Enhanced Ant Lion Optimization Algorithm and its Application in Antenna Array Synthesis

Applied Soft Computing ◽

10.1016/j.asoc.2017.05.007 ◽

2017 ◽

Vol 59 ◽

pp. 153-173 ◽

Cited By ~ 18

Author(s):

K.R. Subhashini ◽

J.K. Satapathy

Keyword(s):

Antenna Array ◽

Optimization Algorithm ◽

Ant Lion Optimization ◽

Ant Lion ◽

Lion Optimization Algorithm ◽

Antenna Array Synthesis

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A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system

Materials Testing ◽

10.1515/mt-2020-0075 ◽

2021 ◽

Vol 63 (5) ◽

pp. 442-447

Author(s):

Hammoudi Abderazek ◽

Ferhat Hamza ◽

Ali Riza Yildiz ◽

Liang Gao ◽

Sadiq M. Sait

Keyword(s):

Optimization Algorithm ◽

Search Algorithm ◽

Spur Gear ◽

Ant Lion Optimization ◽

Lion Algorithm ◽

Weight Design ◽

Sine Cosine Algorithm ◽

Ant Lion ◽

Teaching Learning ◽

Lion Optimization Algorithm

Abstract Metaheuristic optimization algorithms have gained relevance and have effectively been investigated for solving complex real design problems in diverse fields of science and engineering. In this paper, a recent meta-heuristic approach inspired by human social concepts, namely the queuing search algorithm (QSA), is implemented for the first time to optimize the main parameters of the spur gear, in particular, to minimize the weight of a single-stage spur gear. The effectiveness of the algorithm introduced is examined in two steps. First, the algorithm used is compared with descriptions in previous studies and indicates that the final results obtained by QSA lead to a reduction in gear weight by 7.5 %. Furthermore, the outcomes obtained are compared with those for the other five algorithms. The results reveal that the QSA outperforms the techniques with which it is compared such as the sine-cosine optimization algorithm, the ant lion optimization algorithm, the interior search algorithm, the teaching-learning-based algorithm, and the jaya algorithm in terms of robustness, success rate, and convergence capability.

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