An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine

Medical image processing technique are widely used for detection of tumor to increase the survival rate of patients. The development of computer-aided diagnosis system shows improvement in observing the medical image and determining the treatment stages. The earlier detection of tumor reduces the mortality of lung cancer by increasing the probability of successful treatment. In this paper, the intelligent lung tumor diagnosis system is developed using various image processing technique. The simulated steps involve image enhancement, image segmentation, post-processing, feature extraction, feature selection and classification using support vector machine (SVM) kernel. Gray level co-occurrence matrix method is used for extracting the 19 texture and statistical features of lung computed tomography (CT) image. Whale optimization algorithm (WOA) is considered for selection of best prominent feature subset. The contribution provided in this paper is the development of WOA_SVM to automate the aided diagnosis system for determining whether the lung CT image is normal or abnormal. An improved technique is developed using whale optimization algorithm for optimal feature selection to obtain accurate results and constructing the robust model. The performance of proposed methodology is evaluated using accuracy, sensitivity and specificity and obtained as 95%, 100% and 92% using radial bias function support vector kernel.