A Comprehensive Investigation of Deep Learning Methods for Lung Cancer Detection, Classification, and Forecasting

Abstract

Lung cancer continues to be the primary cause of cancer-related mortality globally, mostly due to the often-asymptomatic nature of its earliest stages, which complicates reliable detection through imaging. Concurrently, contemporary radiology generates substantial quantities of high-quality pictures that may inundate human interpreters. Deep learning (DL) has grown into a revolutionary method for automating and enhancing jobs throughout the lung cancer scanning process, including nodule detection, classification, identification, staging, and risk estimation.