LUNG LOBE SEGMENTATION AND LUNG CANCER DETECTION WITH HYBRID OPTIMIZATION-ENABLED DEEP LEARNING USING CT IMAGES

One of the deadly diseases and a leading cause of death worldwide is lung cancer. Compound tomography (CT) is commonly used to identify tumors and it classifies the phase of cancer in the human body. Detection of cancer disease in the lung at an early stage is quite difficult and is essential to increase the survival patient's rate. In this research paper, a deep learning (DL)-based optimization approach is developed for the detection of lung cancer and lung lobe segmentation using CT scan images. Initially, an adaptive wiener filter is used to pre-process the input images and the segmentation process is done by the pyramid scene parsing network (PSPNet) classifier which is effectually trained using the developed honey badger golden search optimization algorithm (HBGSO). Grid-based scheme is used to identify lung nodules and then the features are extracted. Finally, lung cancer detection is done by the Shepard convolutional neural networks (ShCNN) classifier and is trained using the proposed fractional HBGSO (FHBGSO) algorithm. The FHBGSO-based ShCNN outperforms with the highest accuracy of 93.4%, f-measure of 91.2% and precision of 89.8%. Thus, lung cancer is detected in the earlier stages by the devised scheme.