Lung Cancer Prediction Model from Chest X-Ray Images

Abstract

Lung cancer is one of the leading causes of death globally. Early diagnosis of lung cancer is crucial for treatment and prognosis. Traditional medical techniques, such as chest x-rays, have limitations in the early diagnosis of lung cancer. This paper develops an image classification model for chest CT scans using deep learning with transfer learning techniques. The data is divided into three parts: a training set, a testing set, and a validation set. The development of this model can be applied to improve the efficiency of early lung cancer diagnosis, reduce the risk of human errors, and increase workflow efficiency in hospitals. In this paper, a model is developed to distinguish between normal images and images with lung cancer. This model can potentially assist physicians in accurately and rapidly diagnosing lung cancer.