Optimization of Deep Learning Models to Predict Lung Cancer Using Chest X-Ray Images

Abstract

The study aims to clarify and improve the process of lung cancer diagnosis by improving deep learning techniques, especially neural network technology, given the need to improve the accuracy of lung cancer diagnosis using chest X-ray images, and the enormous potential that deep learning techniques can offer, where learning models can be trained. Deep learning on huge amounts of classified Patients and another group of people without it, as well as demographic data about patients and their medical history, then processing missing or conflicting data, converting imaging images into a format suitable for deep learning, then calculating performance metrics, evaluating the model and then improving it. The results indicated that learning techniques could be improved. Deep research has led to improved ability to predict breast cancer and provide rapid and accurate diagnosis.