Modified Densely Connected U-Net with Improved Convolution Deep Belief Network for Lung Cancer Detection on Chest X-Rays
Keywords:
Chest diseases, lung cancer, NIH Chest-Xray, Modified DenseU-Net architecture, Adaptive Butterfly Optimization Algorithm, Improved convolutional deep belief networkAbstract
Chest diseases are major health issues that affect people's lives. Early detections of chest disorders are to human lives and numerous approaches have been developed to help with this. Early identification of lung cancer has become critical, and image processing and DLTs (Deep Learning Techniques) have made it possible. Existing EASFMC-based segmentation findings are good for photos with minimal color change and no external disturbance. When artefacts are present, however, the algorithm performs badly. The algorithm, for example, mistakenly recognizes the black border, medical gauze, and other dark things as lung cancer. Lung patient scan images were used in this investigation to detect and classify lung nodules, as well as to determine their malignancy level. The NIH Chest-Xray pictures are segmented using the Modified DenseU-Net architecture and the Adaptive Butterfly Optimization Algorithm for hyper parameter tuning (ABOA) The segmentation pipeline given here comprises of two DLTs: Enhanced U-Net, which was originally created for biomedical image segmentation, and Improved convolutional deep belief network (ICDBN) for lung nodule detection with their level. The U-Net model performs semantic segmentation on the images before sending them to the ICDBN for final normal/abnormal classification. The lung nodules are classified and the amount of malignancy is determined with excellent accuracy utilizing this design.
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