Enhancing lung cancer detection accuracy using CNNs and advanced image preprocessing

Lung cancer represents a significant health challenge, impacting thousands of individuals each year. Patients with this condition have a notably low survival rate if they are not identified at an early stage. To tackle this challenge, the importance of early detection supported by artificial intelligence (AI) techniques cannot be overstated. This study presents a computer-assisted system for lung cancer detection, which utilizes a hospital-sourced dataset and employs Convolutional Neural Network (CNN) methodologies. Despite the numerous algorithms developed over the years, achieving accurate predictions has remained a challenge. This investigation presents a methodology employing CNNs to detect abnormal growth patterns in lung tissue. The method employs a robust instrument to improve detection precision and increase the likelihood of identifying irregularities. Manual interpretation frequently results in inaccuracies and incorrect diagnoses. The lung images of both healthy individuals and those with malignant conditions underwent meticulous examination to confirm the methodology's validity. The proposed neural network utilizes an efficient training function in its development. This approach exhibits exceptional detection accuracy, yielding results that exceed those of other current detection methods. The meticulous execution of pre-processing steps and a highly effective training function are responsible for the impressive results.