REINFORCEMENT LEARNING AND NATURAL LANGUAGE PROCESSING-BASED SYSTEM FOR AUTOMATIC IDENTIFICATION OF LUNG CANCER
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This topic concerned lung analysis and the detection of tumors or cancer. Its general object is to discuss noise removal, binary image, inverted image, segmentation, and circles segmented. The selected image can undergo some filtrations and processing of image segmentation. The results of the study and image can be processed and segmented. Lung cancer is one of the deadliest diseases which cause high death rates throughout the world. Lung cancer is an irregular growth of cells that can be characteristically derived from a single irregular cell which may spread to whole part of the lung. CT scan is one of the sensitive methods used in the medical field for treating the patients as compared to MRI and X-rays. Diagnosis of cancer from the computed tomography (CT) images of lung is very challenging for doctors. Computer aided diagnosis (CAD) is another tool for detection that uses computer-generated output as an assisting tool for a clinician to form a diagnosis. The biomedical image processing has better ability to detect lung diseases as it helps in analyzing each image and monitoring the data. MATLAB has been used through every procedure made in this study. In image processing procedures, pre-processing of an image is a necessary process as there is difficulty in detecting cancer cells in an image due to the presence of noise and low-quality of images. Steps like image enhancement, image segmentation and feature extraction methods can be used to reduce the degree of those problems. Reinforcement machine learning algorithm is introduced to increase the accuracy rate of detecting tumor growth in lungs. In future, the CT images are collected from health-care centers and remote places through Internet of Things (IoT)-enabled platform and the image is stored in the cloud servers and obtained for processing at any time. The main purpose of this paper is to assist the doctors to detect and classify lung cancer by using CT images based on reinforcement learning algorithm through IoT platform.
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