ORIGINAL ARTICLE An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine Surbhi Vijh 1 • Deepak Gaur 1 • Sushil Kumar 2 Received: 18 March 2019 / Revised: 11 June 2019 Ó The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 Abstract 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 seg- mentation, 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 optimiza- tion 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 pro- posed methodology is evaluated using accuracy, sensitivity and specificity and obtained as 95%, 100% and 92% using radial bias function support vector kernel. Keywords Lung tumor  Global thresholding  Gray level co-occurrence matrix  Whale optimization algorithm  Support vector machine 1 Introduction Cancer has been observed and considered as disease that may cause maximum death around the world as its difficult to perform diagnosis of patient. The early detection of lung cancer can gradually improve the survival rate after pro- viding proper treatment. On the basis of the cellular structure, lung cancer is divided into two parts named as non-small cell lung cancer and small cell lung cancer (Sluimer et al. 2006). Generally, the stage of tumor is dependent upon the size of tumor and location of tumor lymph node (Lemjabbar-Alaoui et al. 2015). Computed tomography is recommended by the physician when lung cancer symptoms are found and process biopsy (taking cancerous tissue out for microscopic analysis) will be suggested only if there is strong affirmation or evidence of lung cancer (Manikandan and Bharathi 2016). Medical image processing has become very efficient technique for treatment of cancer patient and detection of tumor (Farag et al. 2010). The computer aided diagnosis system consists of preprocessing, segmentation of nodule, extraction of features, optimized selection of feature and detection of nodule abnormality by using classification technique. & Sushil Kumar kumar.sushil@nitw.ac.in Surbhi Vijh Surbhivijh428@gmail.com Deepak Gaur dgaur@amity.edu 1 Department of Computer Science and Engineering, Amity University, Sector 125, Noida, Uttar Pradesh, India 2 Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana, India 123 Int J Syst Assur Eng Manag https://doi.org/10.1007/s13198-019-00866-x