AN INTELLIGENT DEEP LEARNING FRAMEWORK FOR LUNG CANCER PREDICTION

Abstract

Cancer is a multidimensional, multilocational disease, which presents severe health, social and economic impacts globally. Recent identification is paramount and this paper takes advantage of DL, specifically CNNs to forecast and classify lung cancer using medical images, clinical data, and genetic data. The given CNN model demonstrated the 91% accuracy, which is higher compared to other DL and traditional machine learning approaches, whereas precision, recall, and F1-score metrics can definitely indicate its usefulness. The study develops the areas of DL use in cancer risk forecasting, and the errors can be reduced with the addition of physiological parameters and IoM technologies in the future works.