A hybrid model for lung cancer prediction using patch processing and deeplearning on CT images

Abstract

AbstractCancer is a common disease with an increasing mortality rate in recent years. Lung cancer is the most common cancer in men and women alike. It is caused by uncontrolled cell development in the lungs. These cells are divided into two types: benign and malignant. Benign tumours are usually harmless, do not spread to other cells, and have a smooth and regular shape, whereas malignant tumours can be dangerous and spread to other body cells to form a new cancerous nodule with an uneven shape. If lung cancer is detected early, it can be treated. Lung cancer symptoms typically appear in the human body when it is in its final stage, but advanced technology and computer-aided systems can detect it at an early stage. Currently, numerous conventional and machine learning techniques are used for such automated detection systems to detect lung cancer in its early stages, but such automated detection systems do not provide accurate detection and the processing of lung cancer detection takes a long time. As a result, a novel method for detecting lung cancer that employs deep learning techniques for accurate detection while requiring less computation time is proposed. CT images are used in this study because they have less noise disturbance than MRI and X-ray images. Median filtering and patch processing are used to improve image quality on such CT scans. These pre-processed images are then subjected to a clustering segmentation process, which segments the image and feeds it to a CNN classifier. For feature extraction and classification, CNN architecture is used. In the future extraction section, various low-level and high-level features are extracted. The classification layer is in charge of determining whether the provided image contains a malignant, benign, or normal tumour. Finally, statistical parameters like MSE, PSNR, Accuracy, Sensitivity, Specificity, and others were computed and combined with the existing system in this work.