Identifying Early-Stage Lung Cancer Using Convolutional Neural Networks (CNN)

As a result of its rapid global spread, cancer has surpassed all other diseases as the top killer of both men and women. The mortality rate for cancer patients is estimated to be between 80 and 85%. Early detection is crucial for effective treatment. Lung cancer is a particularly daunting and difficult type of cancer, among others. Lung cancer is characterized by the rapid proliferation of tumor cells, which raises the risk of metastasis to other organs and causes damage to normal tissue cells. Early detection of the tumor is critical for achieving a complete cure and determining if it has progressed to cancer. Early prognosis is essential as it has the potential to save numerous lives at risk. Furthermore, an accurate diagnosis aids doctors in commencing treatment promptly. This study proposes a straightforward, uncomplicated, and accurate approach for predicting cancer stage using CT lung images. The process involves taking a CT image, followed by noise removal preprocessing to enhance the image quality. The tumor nodule of interest is then segmented to isolate it from surrounding tissues. Morphological parameters, such as area, perimeter, eccentricity, and diameter, are extracted during feature extraction. Finally, this study recommends using MATLAB for classifying lung cancer into various stages based on tumor size data, which offers greater precision and faster results than other lung cancer prediction systems. The proposed technique for lung tumor detection is simpler compared to other complex algorithms.