Segmentation and Feature Extraction in Lung CT Images with Deep Learning Model Architecture

Recently, lung cancer is observed as the most deadly disease throughout the world with a high mortality rate. The survival rate with lung cancer is minimal due to the difficulty in detection of cancer in early stages. Various screening techniques are available such as X-ray, CT, and Sputum Cytology; here, CT images are considered for identification of the lung tumor. Computed tomography has been widely exploited for various clinical applications. Early detection and treatment of lung tumor can aid in improving the survival rate, and CT scan is the best modality for imaging lung tumor. In many cases, when the nodules are identified, it might be either more advanced or too large to be effectively cured. Physical characteristics of the nodules such as the size, tumor type and type of borders are very significant in the examination of nodules. Lung cancer detection and treatment will be of significant value for early diagnosis. Machine learning classification can benefit greatly from the wealth of research on the use of image processing for detecting lung cancer. In this paper, an effective classification model significant value for early diagnosis is developed. The segmentation in CT images is performed with marker-controlled segmentation with likelihood estimation between the features. The proposed model Markov likelihood grasshopper classification (MLGC) is utilized for the classification of nodules in the CT images. The MLGC model performs the estimation of features and computes the likelihood distance between those features. With the estimated features, grasshopper optimization algorithm (GOA) is employed for the optimization of the features. The optimized features are applied over the Boltzmann machine to derive the classification results. The MLGC model estimates the hyperparameters for the selection of set to derive classification results. The simulation results expressed that the proposed MLGC model achieves the higher accuracy value of 99.5% compared with the existing model accuracy which are AlexNet of 96.35%, GoogleNet as 93.45% and VGG-16 as 92.56%.