Extended and optimized deep convolutional neural network-based lung tumor identification in big data

Lung tumor is a complex disease caused due to the irregular growth of lung cells. A key factor in effective treatment planning is the early detection of lung tumor. Visual similarity between benign and malignant nodules, heterogeneity and low contrast variation are the factors that make accurate cancerous lesion recognition, a very challenging task. In this paper, Optimized Deep convolutional neural network (DCNN) and Fuzzy C-means with Equilibrium optimizer (FCM-EO) is proposed for classification and segmentation of CT lung images. The proposed architecture is comprised of four phases such as preprocessing, feature extraction, classification and segmentation. In preprocessing, the weighted mean histogram analysis (WMHA) is utilized to enhance the quality of images and noise removal. Hybrid Dual tree-complex wavelet transform (DT-CWT) with Gabor filter is proposed in feature extraction to extract the features from the preprocessed images. DCNN model is designed to classify the original images into benign and malignant images. The weight of DCNN model is updated using the Enhanced black widow optimization algorithm (EBWOA). In segmentation, FCM-EO is introduced to identify the tumor regions and remove the outliers from the malignant images. LIDC-IDRI dataset is utilized for the experimental analysis and MATLAB is the implementation tool. The simulation analysis is performed for both the classification and segmentation processes. Accuracy, specificity, sensitivity, precision, F-measure, FROC, DSC, MCC, and IoU are evaluated for both these processes. The experimental results showed the proposed framework is efficient for the identification of tumor from the CT lung images.