EARLY DETECTION OF LUNG CANCER USING DEEP LEARNING

Abstract

Lung cancer is one of the most common and lethal cancers worldwide. Early detection significantly improves the chances of successful treatment and survival. However, traditional diagnostic methods such as imaging and biopsy often require advanced stages of the disease to manifest clear symptoms. With the advent of deep learning, an innovative approach has been developed to identify lung cancer in its early stages using medical imaging and patient data. This paper explores the application of deep learning techniques, particularly convolutional neural networks (CNNs), to the early detection of lung cancer. It reviews existing methods, proposes a deep learning-based model that integrates various imaging modalities such as CT scans and X-rays, and discusses the potential improvements in diagnostic accuracy. The proposed method utilizes data augmentation, feature extraction, and model optimization to achieve high sensitivity and specificity in detecting lung cancer at its earliest stages. The effectiveness of deep learning models is evaluated, and the results indicate that these models hold great promise in revolutionizing lung cancer diagnosis, reducing diagnostic time, and improving patient outcomes.