Optimal deep learning model for classification of lung cancer on CT images

Abstract Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. A Computed Tomography (CT) scan used to find the position of tumor and identify the level of cancer in the body. The current study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA). The deep features extracted from a CT lung images and then dimensionality of feature is reduced using LDR to classify lung nodules as either malignant or benign. The ODNN is applied to CT images and then, optimized using Modified Gravitational Search Algorithm (MGSA) for identify the lung cancer classification The comparative results show that the proposed classifier gives the sensitivity of 96.2%, specificity of 94.2% and accuracy of 94.56%.

[1]  Joel J. P. C. Rodrigues,et al.  Effective Features to Classify Big Data Using Social Internet of Things , 2018, IEEE Access.

[2]  Disha Sharma,et al.  Computer Aided Diagnosis System for Detection of Lung Cancer in CT Scan Images , 2011 .

[3]  Ashraf AbdelRaouf,et al.  BLB (Brain/Lung cancer detection and segmentation and Breast Dense calculation) , 2018, 2018 First International Workshop on Deep and Representation Learning (IWDRL).

[4]  Jaspreet Kaur,et al.  An optimized lung cancer classification system for computed tomography images , 2017, 2017 Fourth International Conference on Image Information Processing (ICIIP).

[5]  Dhoriva Urwatul Wutsqa,et al.  Lung cancer classification using radial basis function neural network model with point operation , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[6]  Sushama Nagpal,et al.  Feature Selection using Gravitational Search Algorithm for Biomedical Data , 2017 .

[7]  Yanning Zhang,et al.  Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT , 2018, Inf. Fusion.

[8]  Qun Wang,et al.  Validation of the Stage Groupings in the Eighth Edition of the TNM Classification for Lung Cancer , 2017, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[9]  El-Sayed M. El-Horbaty,et al.  Classification using deep learning neural networks for brain tumors , 2017, Future Computing and Informatics Journal.

[10]  Hao Wang,et al.  Generalized linear discriminant analysis based on euclidean norm for gait recognition , 2018, Int. J. Mach. Learn. Cybern..

[11]  Mehmet Fatih Akay,et al.  Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..

[12]  Kuldip K. Paliwal,et al.  A deterministic approach to regularized linear discriminant analysis , 2015, Neurocomputing.

[13]  Yanyan Wang,et al.  Adaptive multinomial regression with overlapping groups for multi-class classification of lung cancer , 2018, Comput. Biol. Medicine.

[14]  Hiba Chougrad,et al.  Deep Convolutional Neural Networks for breast cancer screening , 2018, Comput. Methods Programs Biomed..

[15]  David Zhang,et al.  Computerized analysis of tongue sub-lingual veins to detect lung and breast cancers , 2017, 2017 3rd IEEE International Conference on Computer and Communications (ICCC).

[16]  Prionjit Sarker,et al.  Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm , 2017, 2017 4th International Conference on Advances in Electrical Engineering (ICAEE).

[17]  Andino Maseleno,et al.  Optimal feature-based multi-kernel SVM approach for thyroid disease classification , 2018, The Journal of Supercomputing.

[18]  M. S. Al-Tarawneh.  Lung Cancer Detection Using Image Processing Techniques , 2012 .

[19]  Rajashree Shettar,et al.  Image Processing and Classification Techniques for Early Detection of Lung Cancer for Preventive Health Care: A Survey , 2014 .

[20]  K. Gunavathi,et al.  Lung cancer classification using neural networks for CT images , 2014, Comput. Methods Programs Biomed..