IoT with cloud based lung cancer diagnosis model using optimal support vector machine

In the last decade, exponential growth of Internet of Things (IoT) and cloud computing takes the healthcare services to the next level. At the same time, lung cancer is identified as a dangerous disease which increases the global mortality rate annually. Presently, support vector machine (SVM) is the effective image classification tool especially in medical imaging. Feature selection and parameter optimization are the effective ways to improve the results of SVM and are conventionally resolved individually. This paper presents an optimal SVM for lung image classification where the parameters of SVM are optimized and feature selection takes place by modified grey wolf optimization algorithm combined with genetic algorithm (GWO-GA). The experimentation part takes place on three dimensions: test for parameter optimization, feature selection, and optimal SVM. For assessing the performance of the presented approach, a benchmark image database is employed which comprises of 50 low-dosage and stored lung CT images. The presented method exhibits its superior results on all the applied test images under several aspects. In addition, it achieves average classification accuracy of 93.54 which is significantly higher than the compared methods.