Nodule classification using its malignancy and benign in lung cancer prediction: A survey

The accurate and automatic categorization of lung nodule malignancy and benign is a challenging task in modern disease diagnosis. The performance of the lung nodule predictive model gets down due to noisy raw input data and the wrong model. ML and DL based Computer Aided Diagnosis Systems show excellent performance in many healthcare applications. Uncertainty measuring in most Deep learning methods is insufficiently considered. Therefore, to deal with this gap new hierarchical helpful Multilevel feature-fusion system with an uncertainty approachable module for image categorization is created. An accurate nodule detection and early treatment purposes a 3DCNN fusion model is used. A Multi_View_Divide and Rule (MV DAR) system is used to learn from reliable and unclear annotations for lung nodule malignancy predictions on chest CT scans. For benign-malignant pulmonary nodule identification a Semi_ Supervised Deep Transfer Learning (SDTL) system is used. To divide malignant from benign nodules using limited chest CT data multi view knowledge-based collaborative (MV KBC) deep model is used. For reducing the number of false positives, a framework of Ensemble-CNN (E-CNN) is used. With different sizes of lung nodules detection, a 3D Convolutional NN (3D CNN) is used. Then, another 3D CNN is presented for the consequent false positive reduction.