… Due to the importance of segmentation process, in this work enhanced deep neural network
approach utilizes multiple layers to extract the affected region effectively. From the derived …

… Lung cancer is a prevalent malignancy that impacts individuals of … lung cancer images. The
primary objective of this work is to propose a novel approach for the detection of lung cancer …

… For this reason, we propose the enhancement of the chest radiograph images using high …
or the lung cancer. The detection is performed by utilizing the RBFNN classifier with inputs the …

… better clinical outcomes for lung cancer patients. … classifier classifies the extracted lung
nodules as benign or non-cancerous and malignant or cancerous. In this research, the classifier …

… method also yielded higher detection performance for all the classifiers, similar to the … The
gamma value of 0.9 yielded the highest detection performance for all the classifiers, and the …

… Compared to models 1 and 2, the enhancement in specificity in models 3 … classifier with a
broad range of validated predictors may improve the diagnostic accuracy for early lung cancer…

… of lung cancer prophecy and progress of computational methods and to predict lung cancer
… In the past few years, machine learning algorithms are utilized for lung cancer survivability [8…

… enhancement and classification is a big task, especially while performing in medical field.
Enhancing … In this paper images of lungs were taken for find various parameters of the texture. …

… lung cancer detection and prediction algorithm using multi-class SVM (Support Vector
Machine) classifier. … In every stage of classification image enhancement and segmentation have …

… from a lung cancer screening … lung segmentation, selective nodule enhancement, initial
nodule detection, feature extraction, and classification. The selective nodule enhancement …