In recent days people are affected with lung cancer in, and the severe stage of this disease leads to death for human beings. Lung cancer is the second most typical cancer type to be found worldwide. Pulmonary nodules present in the lung can be used to identify cancer metastases because these nodules are visible in the lungs. Cancer diagnosis and region segmentation are the most important procedures because the prosperous prediction-affected area can accurately identify the variation in cancer and normal cell. By analyzing the lung nodules present in the image, the radiologists missed several useful low-density and small nodules, and this may tend to the diagnose process very difficult, and the radiologists needs more time to decide the prediction of affected lung nodules. Due to the radiologist’s physical inspection time and the possibility of missing nodules, automatic identification is needed to address these issues. In order to achieve this, a new hybrid deep learning model is developed for lung cancer detection with the help of CT images. At first, input images like CT images are gathered from the standard data sources. Once the images are collected, it undergoes for the pre-processing stage, where it is accomplished by Weighted mean histogram equalization and mean filtering. Consequently, a novel hybrid segmentation model is developed, in which Adaptive fuzzy clustering is incorporated with the Optimized region growing; here, the parameters are optimized by Improved Harris Hawks Optimization (IHHO). At last, the classification is accomplished by Ensemble-based Deep Learning Model (EDLM) that is constructed by VGG-16, Residual Network (ResNet) and Gated Recurrent Unit (GRU), in which the hyperparameters are tuned optimally by an improved HHO algorithm. The experimental outcomes and its performance analysis elucidate the effectiveness of the suggested detection model aids to early recognition of lung cancer.

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