Lung Cancer Detection using Transfer Learning and EfficientNet B2 Architecture

Abstract

The world's most common cancer-related death is lung cancer, and better patient outcomes depend on early identification. The preferred imaging technique for screening for lung cancer is computed tomography (CT); however, due to its limited sensitivity, it can potentially miss diagnosis and produce false-negative results. Some current methods employ various filter types (Gabour, Median, etc.), which might not offer the necessary precision