Detection of Lung Cancer in CT image using Image Processing

Lung diseases are conditions that specifically affect the lungs and disrupt the breathing process. Lung cancer is a major cause of death in humans globally. Timely identification can increase the likelihood of survival among individuals. Timely diagnosis of the problem leads to a significant increase in the average survival rates for individuals with lung cancer, from 14 to 49 percent. Although computed tomography (CT) is significantly more efficient than X-ray, a comprehensive diagnosis necessitates the use of numerous imaging techniques to mutually reinforce each other. A convolutional neural network (CNN) is designed and assessed for the purpose of identifying lung cancer in CT scans. A densely connected convolutional neural network (CNN) and adaptive boosting algorithm were employed to classify the lung picture as either normal or cancerous. A dataset consisting of 201 lung images is utilized, with a portion of the photos allocated for training and the remaining images used for testing and classification purposes. The experimental findings demonstrated that the proposed strategy attained a high level of accuracy.